=Paper=
{{Paper
|id=Vol-3668/paper15
|storemode=property
|title=Modern State and Prospects of Information Technologies Development for Natural Language Content Processing
|pdfUrl=https://ceur-ws.org/Vol-3668/paper15.pdf
|volume=Vol-3668
|authors=Victoria Vysotska
|dblpUrl=https://dblp.org/rec/conf/colins/Vysotska24
}}
==Modern State and Prospects of Information Technologies Development for Natural Language Content Processing==
https://ceur-ws.org/Vol-3668/paper15.pdf
Modern State and Prospects of Information Technologies
Development for Natural Language Content Processing
Victoria Vysotska
Lviv Polytechnic National University, Stepan Bandera Street, 12, Lviv, 79013, Ukraine
Abstract
The study aims to develop a new method of building computer linguistic systems (CLS) for processing
Ukrainian-language text content for solving various NLP problems based on the application of
intellectual analysis of text flow from information resources. This became possible thanks to the
combination of linguistic analysis methods adapted to the Ukrainian language, improved information
technology for processing information resources, machine learning technology and a set of metrics for
evaluating the effectiveness of the functioning of computer linguistic systems. The peculiarity of such
CLS construction is based on the basic principle of system modularity (presence/absence of basic and
additional modules), which facilitates the development of specific modules according to the
requirements for the relevant processes implementation for solving a specific NLP problem. The
developed methods and tools made it possible to build computer linguistic systems for processing
Ukrainian-language text content to solve a specific NLP problem according to the needs of the
permanent/potential target audience based on the analysis of the history of their actions on the CLS
Web resource. The improved technology of intellectual processing of Ukrainian-language textual
content, unlike the existing ones, supports the modularity principle of the typical CLS architecture for
solving a specific NLP problem and analysing a set of parameters and metrics of system performance by
the behaviour of the target audience. This made it possible to develop a general typical CLS structure
and a conceptual diagram/model of the operation of a typical CLS based on the modelling of the
interaction of the main processes and components. Improvement of methods of processing information
sources (resources), such as integration, management and support of Ukrainian-language content, made
it possible to adapt the process of intellectual analysis of text flow to solving various NLP tasks.
Keywords
Computer linguistic system, intelligent search system, NLP, Ukrainian language, information resource,
system performance metrics, machine learning, target audience 1
1. Introduction
The well-known expression of the English banker, businessman and financier Nathan Rothschild
"He who owns information owns the world" is very relevant today, which has been in use for over
two centuries. The modern century is an era of information technology (IT) and artificial
intelligence, which surround the average person everywhere in everyday life. And where there is
a person, there is a natural language. Therefore, the combination of information technologies,
artificial intelligence and natural language processing is relevant in human society for solving
everyday tasks. The solution of such tasks is entrusted to a modern young scientific direction such
as computer linguistics. The difficulty lies not only in solving non-typical NLP problems but in
adapting or creating new models, methods and technologies for processing a specific natural
language. Each natural language is unique, with its flavour of rules, history, grammar, exceptions,
and features of generating linguistic units to convey meaning. On average, a person studies for
10-15 years to understand everyday life, another 10-15 years to adapt to a profession, and the
language itself and its depth can be studied and explored for a lifetime. There is no such luxury as
time to automate the processes of working out a specific natural language. In addition, the limited
financing of similar projects or their absence at all, competition with well-known companies and
COLINS-2024: 8th International Conference on Computational Linguistics and Intelligent Systems, April 12–13, 2024,
Lviv, Ukraine
victoria.a.vysotska@lpnu.ua (V. Vysotska)
0000-0001-6417-3689 (V. Vysotska)
© 2024 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
the presence on the market of their developed commercial projects significantly reduce the
motivation of scientists' work in this direction. Usually, each successful project for the
development of computer linguistic systems (CLS) is designed for a specific task and at the same
time is one-time and closed (for example, Facebook, Siri, Google Assistant, Amazon Alexa,
Microsoft Cortana, Bixby, Voice Mate, Alisa, Abby Lingvo, Microsoft Word, Grammarly, Google
Translation, PROMT, CuneiForm, Trados, OmegaT, Wordfast, Dragon, IBM via voice, Speereo,
Finereader, Tesseract, OCRopus, etc.) without being able to read the content to willing IT
professionals/specialists. There are quite rare cases when such projects are given open access
and an opportunity to get acquainted with their structure and other content.
The Internet, mobile applications, information systems, and social networks – bottomless
sources of information are constantly present around us. On the one hand, it helps to solve many
everyday and professional tasks, but on the other hand, it complicates the life process due to the
need to navigate in this chaos of information space. In addition, it is a source of manipulation of
people's consciousness through propaganda, fakes both in everyday life (for example, through
advertising), and in information warfare, etc.
Nowadays, much online information is subject to regional censorship in certain territorial
regions due to political, economic, social, religious and other factors, such as to control or manage
the opinion of the people of that region. The reasons can be various factors. At the same time, fake
information is spread both purposefully and randomly/chaotically in the Internet environment.
It is easy for an average person to get lost and navigate in this mass of content flow with opposing
facts and causes of events/phenomena. It is unethical, illegal and impractical to control exactly
what to show or hide (to censor content) among Internet content to the average user in
democratic states. This is one of the first steps in the transition to totalitarianism. But providing
information, for example, to journalists about a possible thematic fake for conducting a
journalistic investigation or warning the average reader about the possibility of disinformation
in this content/resource is, on the one hand, support for freedom of speech, and on the other
hand, giving a person the opportunity to choose what to believe and what not to believe. At the
same time, it provides an opportunity to gain an understanding of events and orientation in a
large flow of information both for solving everyday tasks and adjusting business strategies, etc.
Significant and massive dissemination of (dis)information against the background of the war in
Ukraine without appropriate analysis potentially leads to panic among the relevant
stratum/region of the population, significantly affecting the process of adjusting plans/strategies
of business, social services, etc. Against the background of the information war, a lot of time and
resources are spent on the appropriate collection, analysis and formation of appropriate
conclusions regarding the content of the relevant content. This is also influenced by the language
of the information, which may partially/significantly change the content when translated. KLS
will not be able to completely replace human activity in this direction. However, it can be a
significant helper for quickly forming relevant bases of such content and reacting to local changes
or the dynamics of changes in the content flow, marking certain content as potentially fake in a
certain percentage. The difficulty lies in the language of the content itself. In comparison with
English-language content, Ukrainian/russian languages are quite difficult to automatically
process, especially the extraction and analysis of semantics. Today, there are many computer
linguistic systems for various purposes, even for processing Ukrainian-language textual content.
But these are usually commercial projects of a closed type (there are no publications or access to
the administrative part) and most often they are foreign projects. There seem to be a lot of
publications to understand how the natural language processing process generally works,
especially for English texts. However, applying these models, methods, algorithms and
technologies directly to Ukrainian-language textual content does not lead to almost any positive
result. Already at the level of morphological analysis, a significant conflict arises between the
developed methods and the incoming Ukrainian text - the output is not correct. For example, for
a simple Porter algorithm (stemming) without a corresponding modification, it will not be correct
to separate the base of the word from the inflexion, which will lead to incorrect identification of
the keywords of the texts, which in turn affects any NLP task where it is necessary to quickly
identify a set of keywords (rubrication, search, annotation, etc.). Determining the main processes
and features of the linguistic analysis of Ukrainian-language texts will greatly facilitate the stages
of processing the text flow of content such as integration, support and content management. In
turn, the adaptation of the processes of intellectual analysis of text content with the identification
of functional requirements for the corresponding modules of the CLS will lead to the possibility
of developing a typical architecture of such systems based on the principle of modularity (adding
components depending on the content of the NLP task and the purpose of the CLS).
To solve most NLP problems, the words of the relevant textual content are processed, analysed
and researched as a result of the work of one or more authors in a specific dialect of a certain
language (the best measure of the variation of the author's speech characteristics), of a certain
style (dialogue/monologue) and genre (an auxiliary measure of variation features of the author's
speech) at a certain time, in a certain place, for a certain purpose/function. There are more than
7 thousand languages in the modern world. NLP algorithms are most useful when they are
applied to many languages. Most NLP tools are usually developed for the official languages of
large industrialized countries (English, Chinese, German, russian, etc.) and this is a very limited
range of natural languages (out of a couple of dozen). For most of the world's languages, either
no NLP tools are developed, or no significant attention is paid (surface development) or highly
specialized commercial projects. But usually, most of the content consists of text in more than one
language. Therefore, it is advisable to support the development of NLP tools in several languages
according to their purpose, for example, for the classification of text content in the scientific and
technical Ukrainian language, it is advisable to use a combination of NLP techniques not only for
the analysis of the Ukrainian language but at least English due to the presence of specific
terminology and habits speakers to use English analogues from the subject area.
In addition, most natural languages have several regional, social or professional dialects or
slang/slang. This makes it possible to maintain appropriate dictionaries not only for content
classification but also, for example, for identifying the probable author of the corresponding text.
At the same time, some languages are constantly developing and changing at different speeds,
which significantly affects the quality of processing new modern content. Simply changing the
RE-rules will not solve the problem, as all the old contents of the content will not be rewritten. It
is then necessary to introduce the concept of classification of old/new RE-rules, for example, the
morphological processing of words and the support of relevant dictionaries.
Speakers/writers quite often use multiple languages (based on automatic code-switching
according to the content) in a single communicative written/audio content of the appropriate
genre (news, fantasy novel, scientific article or detective story, etc.) and subject matter (technical,
medical, social, etc.). The source of the text also affects the processing features, for example,
spoken (business meetings, telephone conversations, court proceedings, medical advice,
recording of a public speech, etc.) or official documents (laws, orders, etc.). The text reflects the
demographic and social characteristics of the author/speaker such as age range, gender, level of
education (not only the level of literacy and field of education but the level of depth of knowledge),
origin, socio-economic status, etc. Also, the text reflects the approximate period of the appearance
of the world due to the peculiarities of the language in different periods - each language changes
over time. Since language is so situational when developing computational NLP models, it is
important to take into account the characteristics of the author, the context of the text, the
purpose of the assignment, etc. Ukrainian-language textual content, regardless of style, usually
contains a significant amount of unstructured abstract information. It is a meaningful chain of
linguistic units with a predetermined structure, integrity and coherence. Correct, operative and
full-fledged content analysis of the relevant Ukrainian-language text allows for solving many
modern NLP tasks. Parsing Ukrainian textual content into lexemes based on finite automata and
Chomsky grammar is a classic approach. However, it does not solve the main problems of
processing Ukrainian-language textual content.
The creation of any NLP application for an arbitrary natural language from more than 7,000
known languages and dialects is based precisely on the researched data (large
monolingual/parallel text corpora of more than hundreds of millions of words and linguistic
resources) of a specific language. Only about 20 natural languages (English, Chinese, Spanish and
other Western European languages, Japanese, etc.) have relevant research results and meet the
requirements for the development of different complexity of CLS. Unfortunately, in modern
realities, the Ukrainian language is considered in the international scientific community to be an
exotic language with a low resource index, that is, it does not have enough educational, research
and processed data for the development of modern NLP applications while meeting the relevant
needs of society, in particular, in cyber security (detection of fakes and propaganda, so-called
trolls/bots in social networks, etc.), sociology (analysis of the dynamics of changes in public
opinion on certain thematic issues, etc.), philology (automatic research of large data sets of
different thematic directions and different periods), psychology (analysis of the psychological
portrait of a person based on posts in social networks, identification of post-traumatic stress
disorder in participants of hostilities or occupation, etc.) and in other important areas of modern
Ukraine. The proposed methodology is based on the application of advanced technologies of
linguistic analysis and intellectual processing of Ukrainian-language textual content, further
training of CLS on reliable data and analysis of the obtained results to find features and
regularities of the appearance of linguistic events (keywords, stable phrases, metrics of the
author's style, etc.). The above shows the relevance of research in the direction of building
computer linguistic systems for processing Ukrainian-language textual content to solve various
NLP tasks according to the needs of the target audience of Ukraine and international society.
2. Related works
2.1. The concept of computer linguistic systems
The modern development of IT is at the intersection of globalization and informatization. The
rapid rate of growth of the informatization of society is directly related to the rate of development
and introduction of IT processing of natural language (Natural-Language Processing, NLP), the
main tools of which are computer linguistic systems (CLS). According to [1], there are two
different interpretations of the existing term linguistic system (LS):
1. A set of linguistic units of the corresponding speech level (phonology, morphology, syntax,
etc.) taking into account their unity and interconnection; types of linguistic units and the
rules of their formation, transformation and combination. The identification of language
as a language is attributed to F. de Saussure and is based on the works of W. von Humboldt
and J. Baudouin de Courtenay.
2. A set of oppositions (facts) at the appropriate linguistic level using metalanguage for
description and identification.
In [2], the linguistic information system or LS is defined as a system that an individual uses for
his speech activity. According to the interpretation of the Stanford Encyclopaedia [3], computer
linguistics (Computational Linguistics, CL) is a scientific and engineering discipline for finding
approaches to understanding written and spoken language by computer means, as well as
creating methods for processing natural language. Since language is a mirror of the mind,
computer language understanding also contributes to the understanding of the thought process
and the content of intelligence. If natural language is the most natural and universal means of
communication, then appropriate software (software) with linguistic competence should greatly
facilitate human interaction through computers with each other and information systems (IS) of
all kinds to meet everyday needs, for example in IIS/analysis huge text arrays of data and other
Websites. Accordingly, CLS is intended for solving NLP problems according to user needs. The
main features of CLS are the use of methods of artificial intelligence (Artificial Intelligence, AI),
applied linguistics (Applied Linguistics, AL), system analysis and IT for understanding natural
information when solving various NLP tasks both in everyday human life and modern research
of a specialized scientific direction (Fig. 1). The main objects of computer linguistics are content
- arbitrary semi-structured and partially formalized information, presented orally by speech,
written text, visually/emotionally by facial expressions and gestures, graphically by
emoticons/images and/or by any other means of transmission. Content is a collection of various
types of data (text, sound, service, commercial, additional, etc.), which form a corresponding set
of meta-models (a description of the structure and features of the model's functioning) and
template models implemented within a specific information system (ISO/IEC/IEEE
24765:2010(E), 3.1398, ISO/IEC 15474-1:2002, Information technology). There are quite a lot of
publications and studies on solving various NLP problems for processing English-language texts.
There are significantly fewer studies for Slavic languages, in particular, for Ukrainian. In general,
there are no publications on development recommendations, functional requirements, general
structure and typical architecture of CLS.
CLS
Figure 1: The main modern directions for the synthesis of CLS
Typically, each successful CLS development project is task-specific and both disposable and
closed (e.g. Siri, Amazon Alexa, Google Assistant, Grammarly, Abby Lingvo, Facebook, Voice Mate,
Bixby, Microsoft Cortana, Microsoft Word, Google Translation, PROMT, CuneiForm, Trados,
OmegaT, Wordfast, Dragon, IBM via voice, Speereo, Finereader, Tesseract, OCRopus, etc.) without
the possibility of familiarizing the content to willing IT professionals/specialists. There are quite
rare cases when such projects are given open access and an opportunity to get acquainted with
their structure and other content. Accordingly, research in the direction of analysis and synthesis
of CLS, in particular, for the processing of Ukrainian-language texts for today is relevant and
promising [4]-[9], for example, on https://victana.lviv.ua/.
2.2. General classification of computer linguistic systems
Today, the field of CL is developing rapidly, but most projects are commercialized and disposable.
Therefore, there is no single unequivocal approach, typical general recommendations, advice and
requirements for the design, analysis, development and synthesis of relevant CLS. There is also
no consensus on the typification, categorization and classification of CLS. This makes it much
more difficult to navigate in the chaos of publications and research, which methods and tools need
to be applied to effectively obtain the desired results, in particular, when solving a specific NLP
task of processing Ukrainian-language texts [7]-[9]. For example, according to the classification
by Grammarly, there are only three main types of CLS: analytical, transformational and combined
(Fig. 2). There are many more types and possibilities of CLS than described in [10]-[15].
Computer linguistic systems
Analytical Transformational Mixed
Spam filtering Machine translation Conversational agents
Search systems Error correction Learning the language
Sentiment analysis Speech-to-Text / Ending the story
Essay classification Text-to-Speech
Sarcasm analysis QA-system
Character assessment Summarizing the text
Figure 2: Classification of computer linguistic systems by Grammarly
This list should be supplemented with recommender systems, mass media IS, systems for
analysing the psychological state of a person (for example, IBM Watson™ Personality Insights),
plagiarism identification systems (copyright/rewrite), systems for determining the author's
speech style, speechless access interfaces, sign language recognition systems, etc. Stephen
Hawking (one of the most famous people) used a speech computer for communication [16]-[21].
The IBM Watson™ Personality Insights service provides an API for collecting statistical data and
corporate information from social networks and other e-communication tools. The service uses
linguistic analytics to infer internal personality characteristics of people using e-communication
tools such as e-mail, text messages, tweets, and forum posts.
2.3. Basic NLP tasks of computer linguistic systems
The main criterion for market development and the frequency of use of CLS is the motivation
for the use of intelligent software, and cloud solutions/applications based on NLP, which improve
the service of various customers of all possible areas of human activity and significantly increase
the potential audience of modern IT users without the need to possess special skills and
knowledge for their use. This was influenced by the range of problems that should be solved by
different types/purposes of CLS (Fig. 3) [22]-[27].
The tasks of computer linguistic systems
Speech recognition Speech synthesis Text analysis
Voice cloning Information extraction
Voice control
Command recognition Screen reader Information search
Voice text input Imitation of human singing Analysis of statements
Voice search Кодувальник голосу Tonality of the text
SVR systems Ads generation Text categorization
Diarization Acoustic dialogue user QA systems
interface
Speaker recognition Phrase recognition
Creating electronic music
SSI systems Abbreviations recognition
Text generation
OCR systems Text simplification
Machine translation
Morphological
Smart assistant decomposition
Voice translation
IVR systems Recognition of named
Text referencing entities
Smart home module
Spell check Plagiarism/rewriting
Dialogue systems
Syntactic annotation Speech parts definition
Chat bots Semantic annotation Entity relations
Authorship definition Digest formation
Text corpora
Detection of separate
linguistic units Lexical diversity E-dictionaries
Figure 3: Classification of problems of computer linguistic systems
The main directions of problem-solving for CLS are the analysis and/or generation of texts in
natural language, and the recognition and synthesis of natural speech [27]-[33]. Some of the
current problems are simultaneously attributed to some directions, for example, dialogue
systems rely on such NLP tools as language recognition, content and context selection,
identification of intentions, and then building a dialogue based on the above (ideally, by synthesis
speech). Thus, a smart assistant should solve the problems of speech recognition, text analysis,
text generation and, accordingly, speech synthesis. And machine translation solves the problems
of text analysis, speech synthesis and text generation. For QA-systems (question-and-answer), it
is sufficient to solve text analysis problems. However, these are only conditional assumptions
because each implementation of a specific CLS is usually a closed commercial project, which does
not allow IT specialists to familiarize themselves with the detailed structure of the relevant
systems and implemented NLP algorithms.
2.1. Examples and comparative analysis of known modern CLS
Today, with the rapid development of AI, the accelerated growth of large amounts of data and
knowledge, and the rapid pace of informatization of society, many CLS (software/Web services)
of various purposes have been developed and implemented for solving the appropriate type of
NLP tasks according to user needs [34]-[39]. Leading global companies such as Google, Apple,
IBM, Microsoft, etc. work in this direction. Along with them, other less well-known companies,
including Ukrainian ones, are working on different types of CLS. These CLSs have their advantages
and disadvantages. Let us consider and compare only the most popular world and international
CLS projects, respectively, from each class of NLP tasks (Table 1). Companies such as NASA, IBM,
Apple, Amazon, Microsoft, Google, Yamaha, Grammarly, DARPA, Yahoo, etc. work in the field of
computer linguistics.
Most CLS projects are closed, one-off and commercial. Only individual enthusiasts reveal the
secrets of their projects and provide users and IT professionals with access to their
developments. In addition, most of the developed CLS work with English-language texts, or
several European and Asian languages, the list of which does not include the Ukrainian language.
Table 1
Known tools for solving the corresponding NLP problem
NLP task Speech recognition tool
Speech recognition
Voice control and command a component of Microsoft Windows (Vista, v. 7-11), OS/2 Warp 4 and Mac OS X, as
recognition well as Voice Access, IBM ViaVoice, Microsoft Voice Command, Yandex SpeechKit,
Dragon NaturallySpeaking, Speereo Speech Engine, Lexy, linguistic Voice Pro,
Speech (Apple Macintosh), etc.;
Voice input (set) of data VoiceNavigator, Dragon Naturally Speaking MSpeech (Google Voice API), Voco,
Dictate, SpeechPad (Chrome), VoiceNote II (Chrome), TalkTyper (Chrome, 37
languages, free), SpeechTexter (Google Play), Google Docs (Gmail), Voice Notepad
(Chrome, 120 languages), Odrey (Ukrainian development), VoiceTypist (voice input
in Ukrainian), etc.;
Speech analysis IBM via voice, Dragon;
Voice IIS Google, Yandex SpeechKit Mobile SDK, MediaInsight, etc.;
Subvocal recognition (English: NASA (Ames Research Laboratory) technology from the Ames Research Center in
Subvocal recognition, SVR) of Mountain View (California), led by Charles Jorgensen, etc.;
speech during silence of a person
based on the analysis of
electromyograms
Diarization of the speaker to National Institute of Standards and Technology (NIST), etc.;
identify the part of the speech and
its content to a specific person
from the dialogue set
Speaker recognition (person GoVivace Inc., Barclays, Barclays Wealth, CSELT, etc.;
identification based on voice
features for behavioural biometrics
SSI (Silent Access Interface) as an AlterEgo (Arnav Kapur, MIT Researcher), SpeakUP (Varun Chandrashekhar), NTT
auxiliary tool for creating sound DoCoMo, etc.;
phonation of audio speech or
communication in the presence of
background noise
OCR (optical character recognition) ABBYY FineReader, CuneiForm, Brainware, COCR2, ExperVision TypeReader & RTK,
Tesseract, FineReaderOnline.ru, FreeOCR, GOCR, HOCR, img2txt.com, Microsoft
Office OneNote 2007, Microsoft Office Document Imaging, OCRopus, Kirtas
Technologies Arabic OCR, NovoDynamics VERUS, NewOCR.com, OnlineOCR.ru,
Ocrad, OmniPage, Persian Reader, Readiris, ReadSoft, RelayFax Network Fax
Manager, Scantron Cognition, SILVERCODERS OCR Server, SimpleOCR, SmartScore,
Tesseract, ViewWise, WeOCR, Zonal, etc..
NLP task Speech recognition tool
Synthesis of speech
Voice cloning, voice changing CereVoice Me, iSpeech, Voice Anonymizer, LyreBird, Resemble AI, Voice changer,
Morphvox, SDK packages, Voice Cloning Toolkit for Festival and HTS (Mac, Research
Center for Speech Technologies, Junichi Yamagishi from the University of
Edinburgh) etc.;
Imitation of a singing person using SONiKA, LEON, CUL, MAYU, IA, UNI, AVANNA, MEW, DAINA, KAITO, DEX, OLIVER,
Vocaloid technology from Yamaha YANHE, MEIKO, MEIKO V3, LUMi, MATCHA, AZUKI, MAIKA, FUKASE, MIRIAM, LOLA,
Corporation KAITO V3, CYBER DIVA, Ken, Kaori, Chris, Amy, Haruno Sora, KAITO V5, YANHE V5,
CYBER SONGMAN II, CYBER DIVA II, VY1v5, VY2v5, Yuezheng Ling V5, ARSloid,
Sachiko, SeeU, Kaai Yuki, Yuzuki Yukari, Luo Tianyi, GUMI Native (Megpoid), Clara,
Bruno, Ryuto (Gachapoid), GUMIv3 (Megpoid), VY1 (VocaloWitter), eVY1, VY1
(Mizki), VY1v3 (Mizki), VY1 (i-VOCALOID), VY2 (Yuma), VY2v3 (Yuma), VY1V4,
YOHIOloid, ZOLA PROJECT, IA ROCKS, CYBER SONGMAN, V flower, v4 flower,
Gachapoid V3, Megpoid V4, Rana V4, Hiyama Kiyoteru V4, Kaai Yuki V4, SF-A2 miki
V4, Tohoku Zunko V4, Hatsune Miku V4X, Gackpoid V4, Kagamine Rin and Len V4X,
Macne Nana V4, Tone Rion V4, Nekomura Iroha V4, Luo Tianyi V4, Megurine Luka
V4X, Xin Hua V4, Xin Hua V4 Japanese, Hatsune Miku V4 Chinese, Yuzuki Yukari V4,
galaco NEO, Kokone, Ruby, Tohoku Zunko, Tone Rion, Chika, Merli, Lily, Rana, Xin
Hua, Tonio, Macne Nana, Aoki Lapis, SF-A2 miki, Megpoid English, anon & kanon,
Yuezheng Ling, Hatsune Miku V3 English, Big-AL, Hiyama Kiyoteru, Vocaloid
Keyboard, Pocket Miku, Megpoid GUMI, Miku Append, Megurine Luka, Utatane
Piko, Nekomura Iroha, Prima, Kamui Gakupo (Gackpoid), Kagamine Rin and Len,
Hatsune Miku, Sweet ANN, Kagamine Rin and Len Append, Mo Qingxian, Zhiyu
Moke, Mirai Komachi, Kizuna Akari, Yuezheng Longya, Yumemi Nemu, Otomachi
Una, Xingchen (Stardust), etc.;
Voice encoder based on vocoder Cylonix, Darkoder, Lpc-vocoder, Formulator, AC vocoder, Voctopus, Akai DC
technology and VST-plugins Vocoder, Fruity Vocoder, Steinberg Vocoder, FL Studio, Steinberg Cubase, Cakewalk
Sonar, Buzz Composer, Max MSP, NI Reactor/Generator, etc.;
Screen reader or synthesis of VoiceOver (Apple Inc Mac OS X), Агафон, Narrator (MS Windows), Microsoft Agent,
speech from text GNOME, NVDA, VS Robotics, Window-eyes, JAWS, Festival, AT&T Natural Voices,
Gnuspeech, ESpeak, pVoice (Perl), RSS To Speech, Read Words Eng 4, Digalo
(Acapela ELAN TTS), Nuance RealSpeak (ScanSoft), Sakrament TTS Engine,
Govorilka, Speak Aloud, ToM Reader, CoolReader, Linguatec, Acapela, Oddcast,
iSpeech, Google Translate, 2yхa, Бaлaбoлka, Microsoft Speech Api 4.0, тощо;
україномовними є Lesya (KobaVision/KobaSpeech, Code Factory, NextUp, Nuance
Vocalizer), Розмовлялка, WaveNet, UkrVox, RHVoive,
CyberMova/VymovaPlus/VymovaPro, etc.;
Generating messages/ads HKUST Xunfei, VS Robotics, etc.;
Acoustic dialogue interface based Equalizer for Stephen Hawking (Walter Woltosz, CEO of Words Plus), Tony
on Partner-assisted scanning Proudfoot, Roger Ebert, Pete Frates (founder of the ALS Ice Bucket Challenge), etc.;
technology (voice output
communication aids or Speech-
generating devices, SGDs)
Creation of electronic music Adobe Audition, Final Cut Pro (Apple Pro, Mac), MainStage, Logic Pro, Compressor
(Apple Qmaster, Apple Qadministrator, Share Monitor), Motion, etc.;
Analysis of text arrays of data
Information search or information Google, Yahoo!, Yongzin (China), AltaVista, A9.com (Amazon), LightStorage,
search systems Ask.com (Teoma), Alltheweb FAST-Engine, ALLhave, Search engine site ABC Engine,
ZipLocal (USA, Canada), Neti (Estonia), Yandex (russia, belarus, Turkey, Kazakhstan),
Yahoo Japan (Japan), Walla! (Israel), Seznam (Czech Republic), Sesam (Norway,
Sweden), Search.ch (Switzerland), Rediff (India), Rambler (russia), Pipilika
(Bangladesh), Naver (Korea), Najdi.si (Slovenia), Miner.hu (Hungary), Maktoob
(Arab World), Leit.is (Iceland), Goo (Japan), Fireball (Germany), Egerin (Kurdistan),
Daum (Korea), Biglobe (Japan), Accoona (China, USA ), Youdao, Yippy, WebCrawler,
Swisscows, Startpage.com, Soso, Sogou, Searx, Qwant, Mojeek, MetaCrawler,
Lycos, HotBot, Gigablast, Excite, Exalead, Ecosia, DuckDuckGo, Dogpile, Bing, Baidu,
Voilà, Nomade, Locace, Francité, Ez2find, Abacho, Wseeker, AOL Search, SAPO
(Portugal, Mozambique, Cape Verde, Angola), Google Scholar, Scirus, ArXiv.org,
ScienceDirect, PubMed, PDF Search System (PDFSS), LightStorage (media files ),
GlobalFileSearch (files), Tineye (images), etc.; among Ukrainian search engines, the
NLP task Speech recognition tool
following should be highlighted: Meta, Shukalka, Bigmir, I.ua, Online.ua, TopPING,
UAport, Ukr.net, search.com.uab, etc.;
Expression analysis or content qualitative (Kwalitan, MAXQDA, etc.) and quantitative (TextQuest, Textanz, etc.),
analysis WebAnalyst (Megaputer Intelligence), Autonomy Knowledge Server, Text Miner
(SAS Institute), InfoStream (Elvista, Ukrainian development), Lithium Community
Platform, Meltwater Buzz, etc.;
Development of e-dictionaries Abby Lingvo, ForceMem, dict, Stardict, GoldenDict, WordNet (semantic English
dictionary), ConceptNet, Multitran, Vykislovar, WordNet-Affect, SenticNet,
SentiWordNet, etc.;
Text analytics (text mining), Intelligent Miner for text (IBM), SAS Text Analytics, WebAnalyst (Megaputer
information extraction or Intelligence), Autonomy Knowledge Server, SemioMap (Entrieva), TextAnalyst
intellectual text analysis (Megaputer Intelligence), Text Miner (SAS Institute), Apache OpenNLP (Java),
OpenCalais (Thomson Reuters), Natural Language Toolkit (Python), Galaktika-
ZOOM, InfoStream (Елвіста, українська розробка), russian Context Optimizer
(RCO), Lithium, Ontos (TAIS Ontos, Ontos SOA, LightOntos for Workgroups,
OntosMiner), Paai's text utilities etc.;
Analysis of the tonality of the text SAS Sentiment Analysis, Lithium Social Media Monitoring, InfoStream (Elvista,
Ukrainian development), OpinionEQ, Radian6, OpenAmplify SocialView (Visual
Intelligence), Meltwater Buzz, LIQUID CAMPAIGN Opinion Mining, Social Mention,
Tweetfeel, Twittratr, etc.;
Identification of key words and Feature extraction tool (Intelligent Miner for text, IBM), SemioMap (Entrieva),
persistent phrases (collocations) VICTANA (Ukrainian platform), Oracle Text, InterMedia Text, Galaktika-ZOOM, etc.;
Text categorization Categorization tool (Intelligent Miner for text, IBM), SemioMap (Entrieva),
Autonomy Knowledge Server, TextAnalyst (Megaputer Intelligence), RCO, AskNet,
etc.;
Text clustering Clusterisation tool (Intelligent Miner for text, IBM), SemioMap (Entrieva),
TextAnalyst (Megaputer Intelligence), Vivisimo Nigma, Quintura Searchcrystal, etc.;
Question-and-answer systems ELIZA, Watson (IBM), DrQA (Facebook Research), etc.;
(QA-systems)
Phrase recognition WebAnalyst (Megaputer Intelligence), Oracle Text, InterMedia Text, Google
Translatе, TextGrabber, Translate.Ru, Yandex Translatе, Microsoft Translatе,
Translator Foto - Voice, Text & File Scanner, iA Writer, TextExpander, Odrey
(Ukrainian development), Apache OpenNLP, etc.;
Morphological decomposition Oracle Text, InterMedia Text, iA Writer, TextExpander, Odrey (Ukrainian
development), RCO, Apache OpenNLP, etc.;
Recognition of nouns, collocations, OpenNLP, SpaCy, GATE, SemioMap (Entrieva), Autonomy Knowledge Server, Oracle
idioms, idioms and catchphrases Text, InterMedia Text, Galaktika-ZOOM, DBpedia Spotlight, Apache OpenNLP, etc.;
Definition of parts of language Oracle Text, InterMedia Text, Apache OpenNLP, etc.;
words
Language identification Language identification tool (Intelligent Miner for text, IBM), etc.;
Recognition of abbreviations OpenNLP, SpaCy, GATE, VICTANA (Ukrainian platform), etc.;
Simplification of the text WebAnalyst (Megaputer Intelligence), Poetica, Test-the-Text, HamingwayApp,
Readability, No stop words, Ilya Bierman's typographic layout (Gagadget, Ukrainian
version), Typographer Artemiy Lebedev, LeoBilingua, Forson, etc.;
Identification of Unplag/Unichek, Etxt Antiplagiat, Advego Plagiatus, Plag.com.ua, Plagiarisma,
plagiarism/rewriting or duplication Сontent-watch, StrikePlagiarism.com, TEXT.RU, Edu-Birde, InfoStream (Елвіста,
of text Ukrainian development), etc.;
Definition of relationships between Text Miner (SAS Institute), SemioMap (Entrieva), Autonomy Knowledge Server,
entities Galaktika-ZOOM, InfoStream (Elvista, Ukrainian development), Link Grammar
Parser, Mystem, LingSoft, Cíbola/Oleada CLR, StarLing, MCR DLL, SyTech, etc.;
Solution of lexical polynomiality Oracle Text, InterMedia Text, etc.;
Coreference analysis – definition of TextAnalyst (Megaputer Intelligence), Customer Intelligence Center, etc.;
a set of terminological nominal
entities related to one object,
subject, phenomenon or event
Statistical analysis of the text WordStat, netXtract Relevant, URS, FRQDictW, Lemmatizer Multitran, Textarc,
Ngram Statistics Package (NSP), Rhymes, WordTabulator, etc.;
Detection of individual linguistic Autonomy Knowledge Server, SemioMap (Entrieva), Galaktika-ZOOM, etc.;
units
NLP task Speech recognition tool
Marking and labelling of texts for GenCode, TeX, LaTeX, Scribe, GML, SGML, HTML, XML, XHTML, Textual Analysis
the formation of linguistic corpora Computing Tools (TACT), etc.;
of texts
Generation of scripts/plots for Final Draft, etc.;
plays/television programs/films
Editor for concentration FocusWriter, iA Writer, etc.;
Automatic HTML editor Reformator, etc.;
Creation of concordances WordSmith Tools, MonoConc, Textual Analysis Computing Tools (TACT), ParaConc,
WordSmith Tools Mike Scott, Concordance 2.0.0 R.J.C. Watt, etc.
Generation of text datasets based on speech recognition/synthesis and text analysis
Machine translation Google Translate, Microsoft Translator, PROMT, SYSTRAN, Yandex.Translate, TIDES,
Babylon translator, MT@EC, Trados, OmegaT, Apertium, SDL Trados, STAR
Transitfr, Déjà Vu, SDLX, Abby Lingvo, Socrat, Across Language Server, Crowdin,
GlobalSight, gtranslator, MateCat, memoQ, MetaTexis, Open Language Tools,
Phrase, Poedit, Pootle, Babylon, SDL Trados Studio, SmartCAT, UNMIN, Virtaal,
Wordfast, Anusaaraka, DeepL, GramTrans, IBM Watson, IdiomaX, Moses, Moses for
Mere Mortals, NiuTrans, OpenLogos, etc; Ukrainian development: Trident
Software, Pragma, L-Master 98, Language Master; Utility program GoogleTalk,
Facebook, MSN Messenger, Skype, etc.;
Identification of search spam Google, Yahoo!, AIRWeb, etc.;
(Spamdexing)
Creating a rewrite BIPOD, ReWrite Suite, SeoGenerator, korektoronline.pl, Program for rewriting
(plati.ru), etc.;
Checking spelling and grammar Grammarly (developed by Ukrainian programmers), Microsoft Word, myspell,
(spell checker) aspell, ispell, Orfo, SPELL (Ralph Gorin, Stanford Artificial Intelligence Laboratory),
WordPerfect, WordStar, Firefox, GNU Aspell, Mac OS X, Pidgin, Kmail, Opera,
Konqueror, Google, Online Corrector (Ukrainian development), Draft, Google Docs,
Orfogrammka, Language Tool (Ukrainian orthography), etc.;
Voice translation Speereo Voice Translator, etc.;
Referral Annotation tool (Intelligent Miner for text, IBM), Oracle Text, InterMedia Text, RCO,
etc.;
Syntactic annotation WebAnalyst (Megaputer Intelligence), RCO, LeoBilingua, Forson, etc.;
Semantic annotation TextAnalyst (Megaputer Intelligence), RCO, Customer Intelligence Center, Ontos,
etc.;
Formation of digests InfoStream (Elvista, Ukrainian development), etc.;
Latent Semantic Analysis (LSA) patent from Lynn Streeter, Karen Lochbaum, Thomas Landauer, Richard Harshman,
George Furnas, Susan Dumais, Scott Deerwester as Latent Semantic Indexing (LSI),
etc.;
Spam filtering and email routing Kaspersky Anti-Spam, Apache SpamAssassin, AntispamSniper (The Bat!), etc.;
Stylometry (classification by style Emma, VICTANA (Ukrainian platform), etc.;
and genre)
Linguometry netXtract Relevant, WordTabulator, Ngram Statistics Package, Rhymes, Langsoft,
VICTANA (Ukrainian platform), etc.;
Glottochronology VICTANA (Ukrainian platform), etc.;
Evaluation of readability WebFX Readability Test Tool, Automatic Readability Checker, Readability
Calculator, Perry Marshall, StoryToolz, Readability Checker, Word Counter, Joe’s
Web Tools, progaonline.com, ru.readability.io, copywritely.com, glvrd.ru, Advego,
turgenev.ashmanov.com, etc.
Mixed direction of speech recognition/synthesis and text analysis
Smart assistant Google Assistant, Siri (Apple), Amazon Alexa, Yandex Alisa, Robin (Audioburst), Vani
Dialer (Bolo International Limited), Assistant Dusya (UseYoVoice), Marusya
(VK.com), Okey Bloknotik (Dmitriy V. Lozenko), MYRI (BlueTo), Cortana (Windows
10), Horynich, AGGREGATE, Typle (Windows), etc.;
IVR systems (Interactive voice VoiceNavigator, VoiceKey.IVR, (Customer Engagement Platform), etc.;
response, interactive voice
response, voice menu system)
Smart home module Apple Siri, Google Home, Facebook M, Xiao Ai, Amazon Alexa, Microsoft Cortana,
Sonos One, Yandex Alice, etc.;
Dialogue system or speech agent, GUS system, CSLU, NLUI, LinguaSys, modules in modern games, Olympus,
SA Nextnova, Quack.com, NADIA, etc.;
NLP task Speech recognition tool
Creation of chat bots services SendPulse, Flow XO, ManyChat, Chatfuel, MobileMonkey,
ChatbotsBuilder, Botmother, ChatBot.com, etc.;
Determination of authorship of Emma, Linguanalyzer, Attributor, SMALT, Anti-plagiarism, Fresh Eye, etc.;
texts
Analysis of the psychological IBM Watson™ Personality Insights, Avtoroved, etc.;
profile of the author
Analysis of scientific literature NaCTeM services (National Center for Text Mining, Manchester and Tokyo
(determination of novelty and Universities), BioText (School of Information, University of California, Berkeley,
relevance, semantic search, USA), TAPoR (University of Alberta, Edmont, Canada), etc.
identification of homonyms, etc.)
CLS ELIZA (Fig. 4) is one of the first examples of solving the NLP problem of conducting a dialogue
between a computer and a user, imitating the answer of Rogersky's psychotherapist [39]-[43].
Figure 4: Examples of dialogues in CLS ELIZA
Unfortunately, this dialogue is limited in its structure and intended only for English-speaking
participants [39]. ELIZA is a classic CLS that applies a pattern to identify English phrases of the
form You are X and transforms them into typical questions like What makes you think I am X?
(where X is an arbitrary string of English words from the user). This is an imitation of a dialogue
without semantic analysis to understand the content of questions from the user. In [40], the
author noted that ELIZA implements one of several dialogue genres where listeners can act as if
they know nothing about the world around them. In the beginning, most users of ELIZA came to
believe that the system understood them and their problems, even after the explanatory
publication [41]-[43]. This is one of the first attempts to implement chatbots, which are now filled
with modern social networks, services and e-commerce systems.
Of course, today's conversational agents are much more than entertainment; they can answer
questions, book a flight, or find restaurants, the functions they rely on are much more complex to
understand than the user's intent [39]. However, the simple, model-based techniques used by
ELIZA and other chatbots are critical to solving today's NLP problems. But for chatbots in the
Ukrainian language, the usual use of templates makes the process of imitating communication
impossible due to the presence of word changes (by case, tense, plural, etc.) depending on the
context. Without a simple morphological, lexical and syntactic analysis, constructing an answer
or question in Ukrainian in such CLS is not an acceptable result. In addition, regular expressions,
text normalization, tokenization, lemmatization, stemming, segmentation, and editorial distance
calculation should be used for text templates [44]. Regular expressions are used to identify a
sequence of characters to be extracted from previous user queries. For this purpose, word
tokenization is used to separate them from the main text (simple identification of word
boundaries by the presence of spaces and punctuation marks is an insufficient process for
extracting phrases like проспект Червона Калина (Chervona Kalina Avenue), Улан-Уде (Ulan-
Ude), Алма-Ата (Alma-Ata), Південна Корея (South Korea), Івано-Франківськ (Ivano-
Frankivsk), село Залізний Порт (Zalizniy Port village), місто Гола Пристань (Gola Prystan
city), місто Кривий Ріг (Kryvyi Rih city), Кам'янець-Подільська фортеця (Kamianets-Podilskyi
fortress), etc. or type abbreviations, і т.п. (etc.), т.д. (etc.), англ. (English), грн. (UAH), and various
abbreviations, for example, CLS, and NLP). Also, today, when tokenizing, you need to take into
account various punctuation marks for conveying emotions in the form of emoticons, for example,
:), :(, :)))) etc. or hash tags such as #lpnu, #friends. The presence of stylistic errors, such as the
absence of spaces between words or appropriate punctuation marks, complicates the
tokenization process. Text normalization is transforming it into a convenient standard form for
the perception of the CLS user, taking into account and matching all inflexions for all words in the
sentence. Text normalization also uses segmentation or parsing – breaking the text into separate
sentences using punctuation marks as signals. In lemmatization, the same words are identified
by analyzing their roots, despite their difference, for example, the words біжать (run), бігли
(ran), забігли (ran, etc. are forms of the verb бігати (to run). Stemming is a simpler version of
lemmatization, where words are shortened to the base by simply dropping suffixes and/or
inflexion. For the speed of processing texts in Ukrainian, it is better to use stemming (IP based on
key words and stable word combinations), and for the accuracy of the obtained results -
lemmatization (identification of plagiarism and rewriting). To compare words with scattered
chains of symbols, a metric is used - editorial distance, which determines the degree of similarity
of the analysed linguistic units based on the number of necessary edits (insertions, deletions,
replacements) to replace one sequence of symbols with another. It is used most often in
identifying and correcting errors, determining the level of plagiarism-rewrite, IP, generating a
text rewrite, recognizing the language/speech of a specific person and machine translation. The
following categories of machine translation systems are distinguished: statistical (Statistical
Machine Translation, SMT), based on grammatical rules (Rule-Based Machine Translation,
RBMT), and hybrid systems. But in each of them, computational semantics analysis methods are
used to convey the specific content of the text when translating it into another language –
different ways of modelling the meanings of words, phrases, sentences, and fragments of texts.
Computational semantics are divided into distributive, ontological, formal, operational, and
traditional. In particular, distributive semantics is used to determine the meaning of a linguistic
unit based on the statistics of the combination of words in large text corpora of a certain subject
[45]-[54]. In ontological semantics, semantic dependencies of linguistic units of the context are
calculated to form a set of knowledge. Formal semantics is used to describe the meanings of
expressions through mathematical logic and Boolean algebra. Operational semantics allows you
to describe a set of text sentences as a set of commands for controlling some process of generating
events or functioning of an executive device. Traditional semantics describes the meaning of
linguistic units of the text using special interpretation languages. Each of them has its advantages
and disadvantages, especially for syntactic natural languages like Ukrainian.
3. Materials and methods
3.1. Structural diagram of linguistic analysis of textual content
Any text in natural language contains a significant amount of abstract informal unstructured
content data. This is a meaningful chain of symbolic (linguistic) units with a set of appropriate
properties 𝑝𝑗 for solving certain linguistic problems (Fig. 5) [54]-[63], as:
number of sentences, words, words per sentence, etc.;
size and placement of paragraphs;
word length and position of the word in the sentence;
the number of syllables in a word and the number of word contents;
ratio of consonants and vowels;
word depth in the sentence dependency tree;
N-grams and morphemes: affixes, roots, endings;
is the word capitalized/hyphenated/compound;
grammatical categories of different POS, etc.
Analysis Synthesis
Processing
Forming the Logical
plural of text Representation Annotated
Relevant content
of a data array content database
Pragmatic
Graphemic Pragmatic
management
Formatting Information
Irrelevant content Semantic resource
Phonological Duplication
detection
Lexical Classification Syntactic
Result generation
Morphemes Formation of
dictionary Morphological digests Morphological
Semantic Identification of
segmentation keywords and
Syntactic Lexical
Construction of topics
Sentence the SA ontology
construction rules Формування
Semantic Phonological
знань про ПО Vector formation
Formation of
Pragmatic knowledge about Graphemic
subject area
Linguistic
Processing of text Definition of Text generation Dictionary of
dictionary
corpus linguistic units rubrics
Linguistic analysis
Figure 5: Structural scheme of linguistic analysis of textual content
During linguistic analysis in CLS, different levels of natural language text analysis are used to
solve specific problems [56]:
segmentation – tuples of linear chains of characters delimited by appropriate punctuation
marks;
stemming – sets of linear chains of morphological structures;
tokenization – a linear sequence of chains of symbols (words, etc.);
parsing – a network of interconnected structural units in these sentences (grammatical –
lexical – phonological categories).
3.2. States and properties of computer linguistic systems
Any CLS state is determined by a tuple of main properties at a specific moment of time or
activity of the corresponding NLP process [55]-[59]:
𝑠𝑖 = (𝑝𝑖1 , 𝑝𝑖2 , … , 𝑝𝑖𝑚 ), 𝑖 = 1, 𝑛, (1)
where 𝑠𝑖 is the corresponding і-th state at a specific time 𝑡𝑙 from the set with power |S|=n, 𝑝𝑖𝑗 is
the corresponding 𝑖𝑗-th property of the state from the set with power |P|=m, which determines
the behaviour of CLS:
𝑝𝑗 = (𝑟𝑖𝑗1 , 𝑟𝑖𝑗2 , … , 𝑟𝑖𝑗𝑣 ), 𝑗 = 1, 𝑚, (2)
where 𝑟𝑖𝑗𝑘 is the corresponding parameter of the specific property 𝑝𝑖𝑗 for the state 𝑠𝑖 .
For any CLS, the state 𝑠𝑖 can be one of the natural language processing processes, for example,
the identification of key words and/or stable phrases for the next state 𝑠𝑖+1 of the system as a
rubric of a text array of data. Accordingly, the properties of the state 𝑠𝑖 are morphological 𝑝𝑖1 ,
lexical 𝑝𝑖2 and syntactic 𝑝𝑖3 , in some cases, for the accuracy of the analysis, there may be semantic
ones, etc. Then, for the property 𝑝𝑗 , a set of parameters will be determined for the corresponding
text analysis, depending on the specific NLP task. According to these parameters, the strategy of
CLS functioning at the current moment is specified [60]-[66]. For example:
the parameters of the morphological property 𝑝𝑖1 are N-grams and morphemes: roots
𝑟𝑖11 , endings 𝑟𝑖12 , affixes 𝑟𝑖13 ; grammatical categories of different POS 𝑟𝑖14 , word length 𝑟𝑖15 ,
word location in a sentence 𝑟𝑖16 , number of syllables in a word 𝑟𝑖17 , number of word contents
𝑟𝑖18 , ratio of consonants and vowels 𝑟𝑖19 , etc.;
the parameters of the lexical property 𝑝𝑖2 are the location of the sentence in the test 𝑟𝑖21 ,
the location of the word in the sentence 𝑟𝑖22 , the weight of the word 𝑟𝑖23 , the weight of the
sentence 𝑟𝑖24 , the base of the word 𝑟𝑖25 , the inflexion of the word 𝑟𝑖26 etc.;
parameters of the syntactic property 𝑝𝑖3 are the depth of the word in the dependency tree
of the sentence 𝑟𝑖31 , the location of the word in the sentence 𝑟𝑖32 , the number of contents of the
word 𝑟𝑖33 , the number of words per sentence 𝑟𝑖34 , the number of words 𝑟𝑖35 and sentences 𝑟𝑖36 ,
whether the word is a capital letter 𝑟𝑖37 / with a hyphen𝑟𝑖38 / compound𝑟𝑖39 etc.;
parameters of the semantic property 𝑝𝑖4 are the number of word contents 𝑟𝑖41 , the depth
of the word in the tree of sentence dependencies 𝑟𝑖42 , the size of paragraphs 𝑟𝑖43 , the placement
of paragraphs 𝑟𝑖44 , paragraph weight 𝑟𝑖45 etc.
Depending on the property tuple 𝑝𝑗 , CLS behaviour is determined, that is, the implementation
of a set of rules (activation of actions or events) for the implementation of a specific NLP process
to achieve a certain goal depending on the input text data. Accordingly, the event 𝑜𝑙 is a change of
one property to another 𝑝𝑖𝑗 𝑝𝑖𝑘 or 𝑜𝑙 : 𝑝𝑖 𝑝𝑗 according to the fulfilment of certain conditions 𝑈
for the input analysed text 𝑋 and the intermediate processed text 𝐶:
𝑝𝑖 = 𝑜𝑙 (𝑝𝑗 , 𝑈, 𝑋, 𝐶). (3)
Action 𝑑𝑔 is the process of activation of an event 𝑜𝑙 by another event 𝑜𝑣 in CLS:
𝐶′ = 𝑑𝑔 (𝑜𝑙 ∘ 𝑜𝑣 ). (4)
The more complex the language (morphology, syntax, etc.), the more difficult it is to automate
the processing of relevant texts in natural language. In addition, for languages such as Ukrainian,
there are no standardized rules and dictionaries for processing tests in natural language for
solving the corresponding NLP tasks. Many scientific linguistic schools and IT specialists are
working on the creation of Ukrainian dictionaries and rules for processing Ukrainian texts. But
usually, these are linguists and philologists who are not familiar with the features of specific
modern tools, such as programming languages, machine learning methods, BigData analysis, etc.
There is a colossal gap between the research results of philologists and applied linguists on the
one hand, and IT specialists on the other for developing Ukrainian-language tests. In addition,
today quite a few NLP tools for the Ukrainian language have been implemented and implemented
for public access.
3.3. Classification and features of the main properties of states of the computer
linguistic system
Each state 𝑠𝑖 CLS for solving a specific NLP problem uses several or all levels of NLP processes to
form a tuple of main properties 𝑠𝑖 = (𝑝𝑖1 , 𝑝𝑖2 , … , 𝑝𝑖𝑚 ), 𝑖 = 1, 𝑛 [67]-[72]. The content of the speech
of any person, regardless of the specific presentation of information (written or audio), is
transmitted by each of the six properties 𝑝𝑖𝑗 of the NLP process or the level of analysis of human
language, regardless of origin (Fig. 6):
𝑆𝐿𝐴 = 𝑑(𝑝I , 𝑝II , 𝑝III , 𝑝IV , 𝑝V , 𝑝VI ). (5)
I. Phonological level Organization and interpretation of speech sounds
II. Morphological level Identifying and analyzing word structure and form
III. Lexical level Division into chapters, paragraphs, sentences, words
IV. Syntactic level Words analysis as grammatical structure of sentence
V. Semantic level Determining the sentence meaning in text context
VI. Pragmatic level Interpretation of sentences in appropriate contexts
Figure 6: Classification of the main subprocesses of natural language processing
The main NLP tasks are closely related. Therefore, some of the processes in CLS for solving
various NLP problems are similar or even partly the same. For example, for NLP tasks of machine
translation, correcting grammatical errors, identifying keywords, text classification, etc., it is
necessary to apply morphological and syntactic analyses of the text. The process of categorizing
the text necessarily includes the process of defining keywords. The processes of abstracting and
semantic annotation include not only morphological and semantic analysis but also semantic
analysis and definition of keywords. Any text analysis should include the lexical level. In addition,
each level of text analysis for solving a specific NLP problem can consist of different sequences of
steps and their number. NLP methods are used for relevant analyses within the framework of the
solution of a specific NLP problem. For convenience, the main subprocesses of NLP are divided
into linguistic categories, which are solved by certain methods (Fig. 7). Rules for processing
textual content are generated according to these methods. For more effective NLP content, it is
necessary and sufficient that CLS implements the maximum possible number of modules of
relevant speech levels for solving a specific NLP problem. Each of the relevant levels of text
content analysis has its own set of methods for achieving effective specific results depending on
the language of the text.
The main sub-processes of NLP General phonology
Phonological Descriptive phonology
Morphological Historical phonology
Lexical OCR Segmental phonology
Syntactic Parsing Speech-To-Text Supersegmental
Induction Text-To-Speech Phonetic analysis
Semantic
Speech Phonemic analysis
Pragmatic Violation
segmentation
Segmentation
Discourse Tokenization
Linguistic semantics Lemmatization
NLP
applications Relational semantics Marking Stemming
Figure 7: Classification of the main methods of natural language processing
For example, when determining a set of keywords in an English text, parsing, stemming, and
various statistical methods are used to analyse the frequency of use of noun group words and
their distribution in the text. Accordingly, for Ukrainian-language texts, when defining keywords,
the simple stemming algorithm must be replaced with a modified stemming algorithm due to the
presence of a large number of inflexions in the analysed text to identify the nominal group. In
addition, it is not necessary to compare words, but the bases of noun group words, since in the
Ukrainian language keywords are often determined not only by the sequence of words, but their
mutual permutation in different cases is possible (for example, пошук інформації (information
search) – основи пошук інформац, інформаційний пошук (information search) – основи
інформац пошук, пошуку інформації (information search) – основи пошук інформац, etc., that
is, cutting off not only inflexions but also suffixes to bring to the base of the word).
3.4. Classical approaches and trends in natural language processing
Induction, deduction, the method of hypotheses, analysis and synthesis, observation,
idealization, modelling, and formalization are used to study natural language. In addition,
specialized approaches are used to study the phenomena and regularities of a specific natural
language as an object of computer linguistics (Fig. 8). These approaches make it possible to define
a set of procedures and algorithms for the analysis of speech phenomena to solve a specific
problem and, accordingly, to check the obtained results during experimental testing. Usually, for
a specific NLP task, a hybrid approach is used as a combination of several different approaches
(Fig. 8). For example, the methods of statistical analysis, probabilistic modelling and ML, along
with the linguistic approach, are used to determine the authorship of a text, the stylistics of an
individual author, in deciphering, shorthand, language didactics, abstracting, removing polysemy
and IP. Statistical methods are used in content analysis to identify the state of social
consciousness or emotional colouring to promote relevant political and/or commercial
advertising in social networks.
The main classical approaches of NLP
General approach Mathematical approach
Symbolic Quantitative Combinatorial
Linguistic Statistical analysis Language formalization
Graphematic Informativeness analysis Languages and automata
Morphological Probabilistic modeling Petri nets
Syntactic Cryptolinguistics Lexicography
Semantic Machine learning Stylemetry
Regular expressions Method of auxiliary Linguometry
vectors
Bag of words Glottochronology
Neural networks
N-gram models Regression analysis
Deep learning
Hidden Markov Connectivist
model
Latent semantic
Bayesian approach Conditional random fields
Figure 8: Classification of the main approaches to natural language processing
In linguistic monitoring, in addition to the listed set of methods, regular expressions and a bag
of words are used to study the functioning of language in a specific scientific, political or mass
media discourse. The purpose of monitoring is also the identification of foreign language
borrowings, plagiarism/rewriting, grammatical/stylistic errors, vocabulary of
emotions/feelings, thematic/spatial/temporal vocabulary, etc.
3.5. General classification of research directions for NLP problems
Appropriate approaches are used to solve specific NLP problems in typical CLS systems in the
appropriate areas of research (Fig. 9). But when solving each NLP problem, when applying
specific approaches, depending on the research language, different sets of tools are used to
successfully and effectively achieve the set goal. For example, the analysis and identification of
psychological effects laid down by the author of textual content depend on the availability of a
personalized dictionary of the author and a sentiment dictionary of this region (not all words
have the same emotional colours and in different languages and different regions, even different
people of specific people - a simple translation will not help to get a real description of a person's
psychological state). For example, according to the BigFive model, 5 indicators of a person's
psychological state are determined based on his comments on social networks for a certain
period, in particular, levels of Extraversion, Introversion or Ambiversion, Benevolence,
Agreeableness, openness to experience, Openness, Neuroticism, and Conscientiousness. To
analyse the level of Extraversion, lexical measures are studied in the form of an analysis of the set
of marker words used in the texts, which respectively reflect the features of a specific type. One
set of markers is classified as active, sociable, talkative, sociable, sociable, and another set as
reserved, quiet, passive, thoughtful, etc. Markers can be not only adjectives and nominal groups,
but also verb groups in a certain tense as a description of actions in time (active or, accordingly,
passive). At this stage, complexity arises in syntactic and semantic analysis, depending on the
language of the author of the text. In the English-language text, especially in spoken dialogues,
there is a clear order of word groups (noun, verb), compared to Ukrainian texts. In addition, the
average sentence length is significantly shorter in the English-language text. Therefore, it is easier
for them to build a syntactic tree of dependencies to analyse the meaningfulness of markers, and
not just their presence (as in the well-known quote from a fairy tale - where in the phrase
someone казнить нельзя помиловать (to be punished cannot be pardoned); the presence of a
punctuation mark in the appropriate place will determine the level of agreeableness of the author
of the catch phrase).
General trends and trends in natural language processing
Cognition Multimodality Elimination of symbolism
Shallow parsing Multilingualism Based on rules
Named entities Semiotics Representational learning
Dependency syntax Psychological effects of features
Semantic roles Genre effects End-to-End system
Coreference Technical effects Syntax
Discourse parsing Ambiguity Semantics
Semantic parsing Cases Syntactic Pragmatics
Reference Meaningful
Figure 9: General classification of research directions for NLP problems
3.6. Additional methods of linguistic research for NLP tasks
Additional methods are used for higher-level NLP applications (Fig. 10). The cognitive-
onomasiological analysis identifies motivators and the motivational base of phraseological units
for the interpretation and modelling of the SA knowledge structure of textual information and the
semantic dependence between the motivator and the phraseological unit of the Ukrainian
language. The descriptive method is used as part of PHA (phonological analysis), LA (lexical
analysis), MA (morphological analysis) and SYA (syntactical analysis) as inventory 𝑙1 ,
segmentation 𝑙2 , taxonomy 𝑙3 and interpretation 𝑙4 :
𝐿 = 𝑙4 (𝑙41 , 𝑙42 ) ∘ 𝑙3 ∘ 𝑙2 ∘ 𝑙1 . (6)
The internal interpretation of 𝑙41 groups linguistic units according to multiple criteria. The
external interpretation of 𝑙42 illustrates the connections of a linguistic unit with a meaningful
phenomenon, objects, subjects and simulated events of specific text streams of the content of the
corresponding language.
Methods of linguistic research
Genetic affiliation
Basic Linguistic
Correspondences and anomalies
Induction/deduction Comparatively
historical Modeling archetypes
Method of hypotheses
Modeling and Comparable Reconstruction of language states
formalization Typological Chronological and spatial
Idealization localization of language
Structural
Analysis and synthesis phenomena
Component
Observation Descriptive Genealogical classification
Cognitive and onomasiological analysis
Figure 10: Methods of linguistic research for NLP tasks
The comparative-historical method is used to analyse the kinship of languages based on
external (attraction of data) and internal (correlation of phenomena) reconstruction, linguistic
statistics, and linguistic geography. The comparative method is used to identify the specific and
common characteristics of the analyzed speech texts in the grammatical dictionary and sound
systems based on the comparison to form a criterion as a benchmark for comparing the internal
form, onomasiological structure, and word-forming types at the word-forming level, the
component composition of the values of comparative equivalents at the lexical level for machine
translation systems, dialogue systems and chatbots.
The typological method is used to identify and group the main linguistic features and
regularities of speech (clustering) based on differences and similarities of linguistic
characteristics. For research, reference language is used, for example, syntactic, morphological
and phonetic models, semantic field, grammatical rules, linguistic category, specific language,
artificial language, etc.
The structural method is used to study the structure of speech in the methods of
transformational, direct components, distributive and component analysis. Syntagmatic,
paradigmatic, and epidigmatic relations between sentences, grammes, lexemes, morphemes, and
phonemes are analysed. Distributional analysis identifies features and functional characteristics
of linguistic units taking into account the environment (distribution). Analysis by immediate
components is based on the alternate division of a linguistic unit (sentence phrase word)
into components until the moment when we get indivisible parts. Transformational analysis
identifies semantic and syntactic differences and similarities between linguistic units through
features in sets of their transformations when studying lexical semantics, word formation,
morphology and syntax. Component analysis is used to determine the lexical meaning of a word
as seven (reference-organized set of elementary meaningful lexical units) for the formation of
explanatory dictionaries.
Mathematical (statistics and regularities), psycholinguistic (associative experiment, big five),
sociolinguistic (analysis through questionnaires), etc. Quite interesting results are given by the
methods of psycholinguistic analysis as an associative experiment (free, directed or chain)
through the semantic differential. The latter is a qualitative and quantitative indexing of the
meaning of the word through two-pole scales with gradation by a pair of antonymic adjectives.
3.7. Research methods of cognitive linguistics
Cognitive linguistics combines knowledge and research in psychology and linguistics with IT
tools and AI methods. CL approaches are generative grammar (Generative grammar, author
Avram Noam Chomsky), cognitive linguistics (Cognitive Linguistics or linguistics framework,
author George Philip Lakoff) [73] and integrative cognitive linguistics (Integrative cognitive
linguistics or cognitive semantics). According to George Philip Lakoff, CL is divided into the theory
of conceptual metaphor (Conceptual metaphor theory or the analysis of metaphors) and cognitive
and construction grammar (Cognitive and construction grammar or the analysis of constructions
as in form-meaning with comparison with memes as units of language/speech evolution). George
Lakoff proposes a methodology for building NLP algorithms from the perspective of cognitive
science, together with the findings of cognitive linguistics, with 2 aspects:
1. Apply the theory of conceptual metaphor to understand one content of a linguistic unit
(word, phrase, sentence or text fragment) based on another to identify the author's
intention.
2. Assign relative measures of meaning to the analyzed linguistic unit based on the
information presented before and after the piece of text being analyzed, for example, using
a probabilistic context-free grammar (PCFG). The mathematical equation for such
algorithms is given in US patent 9269353 [74]:
𝑑 (7)
1
𝑤(𝑡𝑁 ) = 𝑝(𝑡𝑁 ) × ( ∑ (𝑝(𝑡𝑁−1 ) × 𝑓(𝑡𝑁 , 𝑡𝑁−1 ))𝑖 ),
2𝑑
𝑖=−𝑑
where 𝑤 is a relative measure of value; 𝑡 is a token, any block of text, sentence, phrase or word;
𝑁 is the number of analysed tokens; 𝑝 is a probabilistic measure of value based on corpora; 𝑑 is
the location of the marker along the sequence of 𝑁 − 1 tokens; 𝑓 is a language-specific probability
function.
According to the classification of L.A. Kovbasiuk CL is divided into the following directions
[73]-[74]:
1. Cognitive poetics – the study of cognitive processes based on which a text array of data is
produced, perceived and interpreted;
2. Frame semantics examines cognitive models and mental spaces (frames);
3. Conceptual metaphor and conceptual metonymy (analysis, identification and
interpretation of content based on another);
4. The theory of semantic prototypes (category structuring and identification of components
based on a given prototype, for example, the prototype of собак (dogs) is a вівчарка
(shepherd) or маламут (malamute), of котів (cats) is a Шотландський висловухий
(Scottish short-eared), of птахів (birds) is орел (an eagle), etc.).
Approaches to the development of cognitive models are used in cognitive, functional and
constructive grammar, computational psycholinguistics and cognitive neuroscience (for example,
ACT-R or Adaptive Control of Thought-Rational - adaptive control of thought-rationality, authors
Christian Lebiere and John Robert Anderson from Carnegie Mellon University). Research
directions of cognitive NLP are part of the cognitive AI approach, including based on neural
models for multimodal NLP.
3.8. Classification of the main ML methods for NLP processes
The clustering and classification of large text arrays of data is usually carried out based on ML
methods (Machine Learning) and big data analysis [67]-[72]. To build such methods, the tools of
graph theory, probability theory, optimization methods, mathematical analysis, numerical
methods, mathematical statistics, and various techniques of working with data in e-form are used.
CLS based on machine learning consists of the main parts as NLP, clustering and classification
(Fig. 11).
Basic ML methods for NLP processes
NLP Clustering Classification
Tokenization TF-IDF Logical (rational)
Lemmatization SVD Empirical
List of stop words Historical (hermeneutic)
Cluster group search
Frequency of words Pragmatic, functional,
Parsing Synomatic teleological
Stemming Typological Taxonomy
Figure 11: Classification of the main ML methods for NLP processes
Researching texts is one of the most difficult tasks for programmers due to the ambiguity of
the meaning of words. Some companies, such as Alchemy and Thomson Reuters, have developed
NLP services and ML algorithms for identifying text content. The company Aylien has offered its
API toolkit for text analysis for the possibility of creating various NLP services.
The API makes it possible to quickly identify headings and main text in a document, highlight
the content and main concepts, and create an abstract or abstract. The data extracted from the
text is stored in JSON format, and Mashape is used to provide access to it. Unfortunately, the tool
works only with English and German languages.
The main NLP tasks are the development of algorithms for extracting and analysing features
of linguistic units of measurement from language and applying them to solve a wider range of CL
tasks. Such signs, in particular, are:
number of sentences, words, words in sentences, etc.;
size and location of paragraphs;
the position of the word in the sentence and the length of the word;
ratio of vowels and consonants;
the number of syllables in the word and the meanings of the word;
word depth in the sentence dependency tree;
composition of morphemes: affixes, roots, endings;
N-grams and grammatical categories of various POS;
word with a capital letter / hyphenated / compound.
3.9. The main problems in processing Ukrainian-language texts
The main problems for the development of CLS for studying the Ukrainian language are
splitting of linguistic units, marking of parts of speech (POS tagging), parsing and pragmatics, that
is, how the context affects the content. Pragmatics studies such features as implicature
(ambiguities of statements, hints, guesses), speech acts, relevance and conversation.
Accordingly, the Workflow for NLP for a typical task is:
1. Research available data and NLP algorithms;
2. Prepare test set and baseline and define metrics;
3. Develop an NLP algorithm: feature design; NLP-pipeline (debugging the flow/source of
information as a pipeline); NLP resources; choose a rule-based/statistical/ML approach;
4. Implement and test solutions;
5. Monitor execution.
An interesting NLP task is to identify or generate viral news headlines in social networks or
online newspapers. There are several features that a potential viral headline should possess:
uniqueness of the name - lack of analogue;
proximity – the presence of a reference to the country/city/institution/region of the news
source;
superlativeness – with an indication of the scale/scope or strength/quality of the impact
on the phenomenon/subject/object/environment;
emotion (sentiment) – use of emotional colouring of language;
surprise – use of unusual phrases/phrases;
prominence – the presence of a reference to prominent persons (people, locations, titles)
or events/actions of these persons.
Semantic text analysis is one of the key NLP problems as a theory of CLS creation. The results
of semantic analysis are used to solve problems in such areas as, for example: automatic
translation systems (Google translation); IIS (Google is completely based on semantic analysis);
philology (analysis of author's texts); trade (analysis of demand for certain products based on
comments on this product); political science (prediction of election results); psychiatry (for
diagnosing patients), etc. Visualization of the results of semantic analysis is an important stage of
its implementation, as it can ensure fast and effective decision-making based on the results of the
analysis. An analysis of publications on the Latent Semantic Analysis (LSA) network shows that
the visualization of the analysis results is carried out in the form of a two-coordinate semantic
spatial graph with plotted words and coordinate documents. Such a visualization does not allow
us to identify groups of related documents and to assess the level of their semantic connection by
words in the text. For groups of words and documents without visualization, only cluster labels
and centroid coordinates were determined.
4. Experiments, results and discussions
4.1. Features of intellectual analysis of content flow
In works [77]-[81] attention is focused on the relevance and perspective of the integration of
information flows based on pragmatic text mining methods for solving several TDZ tasks, in
particular, abstracting, analysis of information portraits, content analysis of texts, formation of
digests, IIS, etc. This is quite an informative work, but it does not reveal the specifics of processing
the texts described in the different languages. So, for the phrase content analysis, you will hardly
find its other variant analysis of content in the English test. In the Ukrainian text, for the keyword
контент-аналіз (content analysis), there are often used equivalents such as контентний
аналіз and аналіз контенту and their analogues змістовний аналіз, аналіз змісту. This
complicates the process of semantic analysis for NLP tasks of extracting information from textual
content, which gives inaccurate resulting data. The process of identification/marking of
keywords/terms, IIS by keywords, and the integration of information flows is significantly
complicated, as textual content in the Ukrainian language can take on different forms due to
declension with changing inflexions depending on the gender/plural of the noun and adjective,
the presence of suffixes, alternation of letters when changing words, etc.
In [82]-[86], for the effectiveness of IIS, it is better to use an ontological approach for English-
language content. It is quite effective in extracting knowledge from Ukrainian texts only if detailed
and correct morphological, grapheme, lexical and semantic analyses are carried out beforehand.
It is possible to build an ontology only with the correct definition and appropriate marking of all
connections between all entities with their further preservation in forms (for verbs in the
indefinite form - infinitive, for nouns in the nominative form of the singular and adjectives in the
form of the nominative case of the masculine gender). That is, for the sentence – комп’ютерна
лінгвістична система розв’язує конкурентну задачу опрацювання природної мови (the
computer linguistic system solves the competitive task of processing natural language), the
corresponding analogue will be in the grammatical tree of dependencies in the form of leaves
according to the syntactic analysis комп’ютерний лінгвістичний система розв’язувати
конкурентний задача опрацювання природний мова. Without analyzing this tree, it is
impossible to identify word dependencies and their subordination in sentences to build a
corresponding ontology automatically based on pressing. In [82]-[86] attention is focused on the
relevance and perspective of the analysis of changes in text content streams based on linguistic
analysis, including for IIS information in the form of text content. The processing of text
information is presented as a set of operators for the formation, management, and maintenance
of a set of text content С. Similarly, to previous works, all methods are given for processing content
without reference to the specifics of a specific language. In most publications on the features of
IIS information, it is recommended to consider the set of analysed content С as a set of subsets of
relevant 𝐶𝑟𝑡 and irrelevant 𝐶𝑟𝑓 content, or found 𝐶𝑠𝑡 and not found 𝐶𝑠𝑓 content, or useful content
𝐶𝑢𝑡 for the end user and useless 𝐶𝑢𝑓 , or frequently visited content by users 𝐶𝑣𝑡 and rarely visited
𝐶𝑣𝑓 or the time of viewing the content is greater than a certain value 𝐶𝑝𝑡 or less than 𝐶𝑝𝑓 :
С = 𝐶𝑟𝑡 𝐶𝑟𝑓 = 𝐶𝑠𝑡 𝐶𝑠𝑓 = 𝐶𝑢𝑡 𝐶𝑢𝑓 = 𝐶𝑣𝑡 𝐶𝑣𝑓 = 𝐶𝑝𝑡 𝐶𝑝𝑓 . (8)
The IIS result of the text content is evaluated according to the relevant criteria as the degree
of relevance 𝑘1 , relevance 𝑘2 , popularity 𝑘3 , credibility 𝑘4 , uniqueness 𝑘5 , etc. Table 2 presents
formulas for determining IIS performance criteria. Each of the listed criteria has its rating scale
for forming the rating of the IIS result [87]. The exact calculation of each of the criteria in a specific
IIS result does not improve its quality. Improving the value of one of the criteria will lead to a
deterioration of the other. Finding a balance of IIS criteria values is a laborious process and does
not yield any positive results. But knowing which of the indicators is better to use for a specific
purpose of IIS will make it much easier to get the expected result.
Table 2
Criteria for forming the result of IIS text content based on [87] and authors research
𝑘𝑖 Name Content Formula
𝑘1 Relevance Correspondence of the number of keywords n of 𝑛 𝑛 𝑛2
, , or for specific content, but for
the found content to the number of keywords 𝑁 of 𝑁 𝑀 𝑀𝑁
|𝐶𝑢𝑡 𝐶𝑠𝑡 |
the IIS request and 𝑀 of the found content, or the everything found then
|𝐶𝑢𝑓 𝐶𝑠𝑓 |+|𝐶𝑢𝑡 𝐶𝑠𝑡 |
ratio of useful to the user to the total
𝑘2 Popularity The ratio of frequently visited and overtime |𝐶𝑣𝑡 𝐶𝑝𝑡 |
content to all relevant content found |𝐶𝑟𝑡 𝐶𝑠𝑡 | + |𝐶𝑣𝑡 𝐶𝑝𝑡 |
𝑘𝑖 Name Content Formula
𝑘3 Topicality The ratio of frequently visited user-friendly content |𝐶𝑣𝑡 𝐶𝑢𝑡 |
to all relevant content found |𝐶𝑣𝑓 𝐶𝑠𝑡 | + |𝐶𝑣𝑡 𝐶𝑢𝑡 |
𝑘4 Certainty Correspondence of the content to the real meaning 𝑘1
, 𝑝 = [0; 1]
and reliability of the source 𝑝𝑠 𝑝𝑠 𝑠
𝑘5 Uniqueness The indicator of the originality of the author's 𝑘1𝑖 · |𝐶𝑠𝑡 |
content data with the found ones ∑|𝐶 𝑠𝑡 |
𝑖=1 𝑘1𝑖
𝑘6 Authenticity Indicator of compliance with the source of origin 𝑘1𝑖 · |𝐶𝑠𝑡 |
· 𝑝𝑠 , 𝑝𝑠 = [0; 1]
and content authorship ∑|𝐶 𝑠𝑡 |
𝑖=1 𝑘1𝑖
𝑘7 Profitability The ratio of the number of returns |𝐶𝑟𝑝 | to the total |𝐶𝑟𝑝 𝐶𝑠𝑡 |
number of views of the content |𝐶𝑣𝑓 𝐶𝑠𝑡 | + |𝐶𝑣𝑡 𝐶𝑠𝑡 |
𝑘8 Completeness The ratio of found relevant content to all possible |𝐶𝑟𝑡 𝐶𝑠𝑡 |
= 1 − 𝑘13
relevant content |𝐶𝑟𝑡 𝐶𝑠𝑡 | + |𝐶𝑟𝑡 𝐶𝑠𝑓 |
𝑘9 Precision The ratio of found relevant content to all found |𝐶𝑟𝑡 𝐶𝑠𝑡 |
= 1 − 𝑘10
content |𝐶𝑟𝑡 𝐶𝑠𝑡 | + |𝐶𝑟𝑓 𝐶𝑠𝑡 |
𝑘10 Noise The ratio of found irrelevant content to all found |𝐶𝑟𝑓 𝐶𝑠𝑡 |
= 1 − 𝑘9
content |𝐶𝑟𝑡 𝐶𝑠𝑡 | + |𝐶𝑟𝑓 𝐶𝑠𝑡 |
𝑘11 Precipitate The ratio of found irrelevant content to all |𝐶𝑟𝑓 𝐶𝑠𝑡 |
= 1 − 𝑘12
irrelevant content |𝐶𝑟𝑓 𝐶𝑠𝑓 | + |𝐶𝑟𝑓 𝐶𝑠𝑡 |
𝑘12 Specificity or The ratio of irrelevant content not found to all |𝐶𝑟𝑓 𝐶𝑠𝑓 |
= 1 − 𝑘11
selectivity irrelevant content |𝐶𝑟𝑓 𝐶𝑠𝑓 | + |𝐶𝑟𝑓 𝐶𝑠𝑡 |
𝑘13 Remainder, loss The ratio of no relevant content found to all |𝐶𝑟𝑡 𝐶𝑠𝑓 |
= 1 − 𝑘8
or silence possible relevant content |𝐶𝑟𝑡 𝐶𝑠𝑡 | + |𝐶𝑟𝑡 𝐶𝑠𝑓 |
𝑘14 Uncertainty The ratio of not found relevant content to all |𝐶𝑟𝑡 𝐶𝑠𝑓 |
= 1 − 𝑘15
possible irrelevant content |𝐶𝑟𝑓 𝐶𝑠𝑓 | + |𝐶𝑟𝑡 𝐶𝑠𝑓 |
𝑘15 Ambiguity The ratio of irrelevant content not found to all |𝐶𝑟𝑓 𝐶𝑠𝑓 |
= 1 − 𝑘14
possible irrelevant content |𝐶𝑟𝑓 𝐶𝑠𝑓 | + |𝐶𝑟𝑡 𝐶𝑠𝑓 |
𝑘16 Pertinence The ratio of the amount of useful content |𝐶𝑢𝑡 |
according to the user's needs to the total amount |𝐶𝑠𝑡 |
of content found
𝑘16 Conformity Relevancy of useful content to found content ∑|𝐶 𝑠𝑡 |
𝑖=1 𝑘1𝑖
|𝐶𝑠𝑡 | · |𝐶𝑢𝑡 |
𝑘17 Satisfaction Performance indicator for integrated assessment 𝑘8 + 𝑘9
of IIS
𝑘18 Functionality IIS quality assessment performance indicator 𝑘8 · 𝑘9
𝑘19 Specification The ratio of the accuracy coefficient to the 𝑘9 /𝑝𝑟𝑐
probability of relevance of random content 𝑝𝑟𝑐 in
the array
𝑘20 Convertibility Failure ratio |𝐶𝑐𝑣 | to the total number of views of |𝐶𝑐𝑣 𝐶𝑠𝑡 |
the content |𝐶𝑣𝑓 𝐶𝑠𝑡 | + |𝐶𝑣𝑡 𝐶𝑠𝑡 |
𝑘21 Novelty Performance indicator for evaluating the quality of 𝑘3 · 𝑘5
found relevant content
If the result is to satisfy the end user in a particular PPI, this is one set of criteria, and even in
it, there are more important criteria and less important ones (the relevance indicator can prevail
over the uniqueness indicator, and to calculate the relevance, accuracy and completeness criteria
are used, taking into account the reading time and frequency visits to the candy store by previous
visitors). If it is necessary to identify a set of Websites where some grey/black SEO methods are
used, then another set of criteria is used, in particular, noise and sediment. One hundred per cent
effectiveness of IIS is impossible due to the subjectivity of the author's content, the presence of
noise due to the use of grey/black SEO technologies for website promotion, and the incorrectness
of creating content search patterns (CSP) due to the complexity of linguistic processing of
languages, in particular, Ukrainian.
4.2. Technologies of intellectual analysis of text flow
One of the widespread IT analyses of the flow of textual content is the integration of data from
different sources (Fig. 12). Data from reliable sources is usually integrated by tag analysis.
However, the complex process of integration is based on extracting information or data from
different sources of content with the growth of NLP methods. Qualitative generation of new
textual content from a set of different in nature, but similar in content, data from different sources
is one of the most relevant and promising NLP tasks today, for example, for successful e-business
management. The stages of generation and application of a set of text content determine the
methodology of collection, filtering, indexing, formatting, structuring of information from
relevant sources, and further storage, processing, support, formation, management, etc., that is,
the main stages of intellectual analysis of the flow of text content (Fig. 13). The process of
intellectual analysis of text flow consists of:
1. content integration based on text recognition and analysis;
2. content management based on text analysis and processing;
3. content support based on analysis and synthesis of information.
Sources Х NLP text Receivers Y
Resource processing 1
Web sites Structured data
2
Data Web servers
repositories
Loosely structured
Text/audio content
data 3
End users
Email Smart assistants
4
Data without a
Search agents
predefined Annotated
structure databases
5
Search
systems
Repositories 6
Social Knowledge base
networks
...
Content Metadata Data repositories
n
authors
Figure 12: The process of integrating test data from X sources into Y receiver
WWW Recognition and analysis Analysis and processing moderator Synthesis and analysis WWW
administrator
Resource 1 Integration rules Rules of text Text synthesis and Resource 1
analytics analysis rules
Parsing/searching
Analysis of Information
Content collection statements portraits
WWW WWW
User database
Formatting DB formation and Thematic plots
database
Content
Resource 2 rotation Resource 2
Text plagiarism
Text translation Content dynamics
Keywords
Content
WWW Orthography Text rewrite
relationships WWW
Customization via
Resource 3 Annotation Dialogue support Resource 3
Annotated
XML/RSS
database
я
Referencing Semantics analysis Screen reading
Resource
database
Duplicates Development of a Learning the system
WWW
WWW web resource
Classification Personalization
Work with PROXY- Resource М
Resource N-1
Tonality of the text server service Generation of ads
Digests
@ Query analysis
and messages
E-mail Search
WWW permanent Resource statistics
Content Creating a report systems
moderator end user
distribution analyst
Resource N Content rating
Form generation
Text recognition Statistics analysis
and analysis rules Questions and
answers rules
Figure 13: General scheme of intellectual analysis of the text stream
4.
The process of content integration ensures the formation of content based on the methods of
content monitoring, content analysis, information extraction and speech recognition from
various sources according to the information needs of the permanent/potential audience (Fig.
14), in particular:
WWW
Recognition and analysis Content
Website 1 integration
Annotated
Scanner Cache Filters Domain
Database of administrator content
cached data database database
WWW
K1 Website 2
C1 Knowledge Content search Collecting and
Content monitoring and data filtering
Unformatted content K2 base
C2 WWW annotating content
The content in XML format
K3
Website 3 Error
C3
Content distribution
correction
.... Content
........ ....
WWW
Content filtering
Website 4
Plagiarism database
CL KM
checker
Content
Duplicate
Sources qualification
removal Content The rule
........ distribution base
WWW
Content
base Website N-1
editor Editing rules
Statistics
WWW editor database
moderator
Website N
Knowledge Moderation of rules
administrator
base and content user moderator analyst
Figure 14: Scheme of integration of text content from different sources
administrator integration rules knowledge base formation of a set of IIS parameters content
database IIS by parameters database of cached data source parsing information extraction
annotated content database content formation content database content distribution
moderator
moderator rules of text recognition and analysis knowledge base content formation from
integrated data content database content systematization content database content distribution
editor content publication Website/Web page
integrated data content collection cached database content formatting content database
plagiarism check and removal content database duplicate removal error correction content
database keyword identification annotated content database content annotation annotated
database content data abstracting content database rubrication content database sentiment
analysis content database digest formation content database content distribution
potential/permanent audience
The content management process is described in the following terms:
User request processing filter database content monitoring content database content
analysis content formatting content presentation Website/web page
The Website content management process is classified according to the relevant criteria for
forming a response to a user request (Fig. 15):
1. Formation of the content of the web page according to a specific personalized request of the
user from the DB at a certain point in time (Fig. 15-Fig. 16). Webpage formation depends on the
specific request of each user of the permanent audience. This leads to a significant increase in the
load on the Webserver with each user request of a permanent audience of the corresponding
Website. The load is reduced by caching frequently requested information in a certain time
according to the previous statistical analysis of the dynamics of requests.
2. Formation of static web pages when edited by the Website moderator (Fig. 17-Fig. 18). Full-
text content monitoring in large databases/databases is inefficient. The problem of
responsiveness and accuracy of content monitoring is solved by IIS in annotated DBs. Effectively
apply content monitoring for IIS text according to CSP (templates, annotations) with weighted
keywords and stable word combinations with the largest weight values. The problem of the lack
of interactive dialogue between the user and the Website is provided not only by the presence of
caching of frequently requested data but also by the analysis of the statistics of this client's
requests for a certain/entire time.
3. Webpage caching according to the analytics of requests (last similar requests) of users and
transitions from IIS with the achievement of visit conversion (Fig. 19-Fig. 21).
forming general
requirements
parameters selection
choice of methods
defining template
requirements
structure formation
no
development
method clarification
query analysis
database monitor
yes
no method compliance
content analysis
Webpage template
development
content creation
no
yes
compliance with requirements formatting
template
exploitation
Webpage formation
yes
correctness check
structure analysis yes
load
yes no
presence of errors caching of a popular
Webpage
error identification
no
Webpage
template moderation presentation
a) b)
Figure 15: Generation of a) template and b) web page at the request of the user
Administrator Information
Administration resource
Moderator settings
content
Database, filters, Content
result rules
Content monitoring presentation
WWW
result
запит Webpage
Request processing Content Caching
answer Content management analysis Content caching
User
Figure 16: The process of managing content at the user's request
Administrator Administration Web-
server
Moderator settings Content analysis Database, rules
content and filters
result
result
WWW Query analysis
request Website X Forming and
Content monitoring submitting the result
answer content management of a request to a page
User
Figure 17: Formation of the static web page during moderation
content input entering conditions
editing Webpage generation
content analysis connection
verification
no no
yes yes
relevant? generation correctness
Webpage filling Webpage filling
Webpage analysis filling analysis
no no
yes yes
correctness correctness of filling
Webpage output Webpage output
a) b)
Figure 18: a) Generation and b) filling of the Webpage during content moderation
Content Cache Website
WWW
content K
distribution and Webpage 1
presentation ...... t WWW WWW
content 3 Webpage 2
The displayed
Content monitoring content 2
WWW
webpage
Webpage 3
Web analytics content 1 WWW
Webpage N
......
Block generation
Base of rules, Formation of search patterns and rules
Content analysis content and Analyst
Content caching filtering
Query analysis and editing
Content analysis and editing
query L query 3 query 2 query 1
content M content 3 content 2 content 1
......
......
Moderators Users
Figure 19: Caching of the generated web page according to query analytics
CLS generates a web page once at a certain moment and stores its image in the DB. The
webpage is stored in the cache for a time 𝛥𝑡 (as long as the content of the webpage for a certain
period 𝛥′𝑡 is not requested by other users). The set of web pages in the cache is updated according
to the history of requests from the permanent audience. Users can access such web pages faster
than waiting for a new webpage to be filled. The cache is periodically updated
manually/automatically: after the expiration of the term 𝛥𝑡, either the Webpage will not be
requested by users for a certain time (Fig. 20), or a significant modification of the
Website/content with the content of these Webpages. Analysis of changes in the dynamics and
time of access to the relevant cache data determines a set of thematic interests of the permanent
audience (Fig. 21). It also determines the speed of development of the needs of end users for the
relevant operational thematic areas of the Website content. An appropriate timely analysis of
such dynamics of changes in requests and the time of audience interest allows us to adjust the
filling of the Website with the relevant content.
request input
content input
editing a request
editing
content analysis
content analysis
no no
yes
check cache
content relevance
database editing Webpage
moderation and
content generation verification
filling analysis
yes Webpage generation
no
yes
yes
correctness of filling
no t