=Paper=
{{Paper
|id=Vol-3171/paper44
|storemode=property
|title=Comparative Analysis of Using Different Parts of Speech in the Ukrainian Texts Based on Stylistic Approach
|pdfUrl=https://ceur-ws.org/Vol-3171/paper44.pdf
|volume=Vol-3171
|authors=Alina Dmytriv,Svitlana Holoshchuk,Lyubomyr Chyrun,Roman Holoshchuk
|dblpUrl=https://dblp.org/rec/conf/colins/DmytrivHCH22
}}
==Comparative Analysis of Using Different Parts of Speech in the Ukrainian Texts Based on Stylistic Approach==
https://ceur-ws.org/Vol-3171/paper44.pdf
Comparative Analysis of Using Different Parts of Speech in the
Ukrainian Texts Based on Stylistic Approach
Alina Dmytriv1, Svitlana Holoshchuk1, Lyubomyr Chyrun2 and Roman Holoshchuk1
1
Lviv Polytechnic National University, S. Bandera Street, 12, Lviv, 79013, Ukraine
2
Ivan Franko National University of Lviv, University Street, 1, Lviv, 79000, Ukraine
Abstract
The work aims to analyse words of different parts of speech in Ukrainian texts to identify the
speaker’s purpose of using certain parts of speech to express his opinion fully. Our analysis
enables better recognition of written texts and the flow of the author’s thoughts by considering
words of different parts of speech. In work, such analogous systems as Intelligent Ukrainian
Text Processing System, Large Electronic Dictionary of the Ukrainian Language (VESUM),
lang-uk microservices, NER annotated corpus, and tonal dictionary of the Ukrainian language
is considered. The system is designed by incorporating use-case, states and activities diagrams
together with program implementation tools such as Python, MySQL and Tkinter. In addition,
the software which analyses Ukrainian texts and calculates the frequency of words of different
parts of speech is presented. It also demonstrates the results of frequency comparison of other
parts of speech based on texts of different styles and then creates diagrams showing its
statistics.
Keywords 1
Ukrainian language, morphological analysis, parts of speech, Ukrainian text
1. Introduction
Recognition of Ukrainian-language texts is only on the initial phase of its development [1-6]. Since
the Ukrainian language belongs to a synthetic group of languages (i.e. syntactic relations within
sentences are expressed by inflexion), it complicates the automatic detection and correction of errors,
automated analysis and synthesis of oral speech, automatic translation and more [7-14]. The mentioned
issues need ways of creating solutions that stimulate a wide range of possible research in this area [15-
20]. Modern investigations focus on developing available tools for Ukrainian language recognition
[21-29]. Most developers perform this work on a volunteering basis and provide online access to their
libraries [15-20, 30-33]. They aim to enable every interested person to be involved in this project. The
Ukrainian language takes 16th place among the most popular language on Wikipedia and trails behind
with the 32nd on the Internet. Developing high-quality Ukrainian-language NLP programs is relevant
and needed in Ukraine [34-42]. A large amount of untranslated professional literature, which makes
Ukrainians read everything in original and creates difficulties for some of them, is another factor that
fosters research in this area. In addition, such programs would provide an opportunity to analyse
Ukrainian social networks and the media, facilitating faster and greater identification of helpful
information and various problems [15-20]. Therefore, providing free access to the created libraries for
NLP of the Ukrainian language is a significant process. It gives extra information for developing
computational linguistics of the Ukrainian language.
Modern programmes developed for the analysis of the Ukrainian text do not cover the whole
spectrum of the problems in the field [1-7]. An important question associated with better machine
COLINS-2022: 6th International Conference on Computational Linguistics and Intelligent Systems, May 12–13, 2022, Gliwice, Poland
EMAIL: alinadmutriv@gmail.com (A. Dmytriv); svitlana.l.holoshchuk@lpnu.ua (S. Holoshchuk); Lyubomyr.Chyrun@lnu.edu.ua (L.
Chyrun); roman.o.holoshchuk@lpnu.ua (R. Holoshchuk)
ORCID: 0000-0003-0141-6617 (A. Dmytriv); 0000-0001-9621-9688 (S. Holoshchuk); 0000-0002-9448-1751 (L. Chyrun); 0000-0002-1811-
3025 (R. Holoshchuk)
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
recognition of the Ukrainian language is to develop new programmes and improve the existed ones
based on the analysis and modification of relevant analogues for the work of English texts [43-62].
The purpose of the work is to analyse words of different parts of speech in Ukrainian-language texts
to determine whether the author prefers to use certain parts of speech to express his opinion fully.
The research object is the construction analysis of the Ukrainian texts considering words of different
parts of speech. The research subject is the analysis of the frequency and sequence of words used from
different parts of speech in Ukrainian-language texts. The analysis can help better understand the
writing of Ukrainian-language texts and the presented course of the author’s thoughts by using words
of different parts of speech. The stylistic analysis based on statistic analysis and machine learning
technology can become a part of the statistical and semantic analysis of the Ukrainian texts, automatic
translation, construction of N-grams, templates of the structure of the sentences, etc. [63-68].
2. Related Works
Analogues’ characteristics, advantages, and disadvantages are searched and analysed to explore the
mentioned question. It includes the following resources and systems, which are considered and
described in detail [15-20]:
• Intelligent system of Ukrainian Text Processing;
• Large electronic dictionary of the Ukrainian language (VESUM);
• Microservices lang-uk;
• Corpus of NER-annotations;
• Tonal dictionary of the Ukrainian language.
2.1. Intelligent system of Ukrainian Text Processing
The intelligent system of Ukrainian text processing was developed in 2019 at the Taras Shevchenko
National University of Kyiv [15]. It implements specific linguistic tasks related to the processing of the
Ukrainian language, namely the preliminary processing of the text, morphological and lexical analysis
of the text. To create such a system, the authors analyse the available means of text processing in natural
language and consider using them in the Ukrainian language. In this system, for preliminary analysis
of the Ukrainian text, NLTK library tools are selected. They are used for tokenisation, detection of
sentence boundaries, deletion of non-text elements (tags, meta-information), e-mail highlighting, file
name selection, compilation, words written with an interval between letters, removal of stop words,
recognition of nominal entities. Regarding the morphological analysis and determination of words
belonging to specific parts of speech, this program gives the following characteristics: lemma, stem,
part of speech, cases, gender, number, being / non-being, time, person and species.
The advantage of the developed system is that it provides opportunities for the complex processing
of Ukrainian texts at three levels of analysis. The disadvantages of the developed system are suboptimal
algorithms for composing words written with spaces between letters (the algorithm uses a dictionary to
identify words written with spaces between letters). A length limit is introduced to reduce the words
search, and the words more extensive than the boundary conditions may contain errors. Also, it may
not consider an incorrect analysis of non-vocabulary words, which is a disadvantage of pymorphy2,
based on the morphological module of the system and simple lexical analysis.
2.2. Large electronic dictionary of the Ukrainian language
The large electronic dictionary of the Ukrainian Language (VESUM) is a dictionary of word changes
in the Ukrainian language. Its main components are the register, the code of word change classes and
rules for generating word forms based on these codes and using elements of program logic [16].
Consider an example of the representation of words lem in this dictionary. The following code
represents the word близнюк [twins]: близнюк /n20.a.p.ke.<, where n20 are nouns of the second
declension of the masculine gender, the key a is the ending -a in the genitive singular, the key p is the
plural form, the key ke is the ending -e in the accusative case.
VESUM performs the tasks of morphological analysis and synthesis. The first is to lemmatise
(reduce a single word form to a lemma) and assign the appropriate grammatical tags. The second one
involves generating all word forms from a particular lemma with the proper grammatical features-tags.
Distinctive characteristics of VESUM [16]:
1. Machine-readable format;
2. Open project;
3. Dynamic nature;
4. Large registers (more than 401 thousand lemmas, from which more than 6 million-word forms
are generated);
5. Exhaustive coverage of proper names (almost 53 thousand);
6. Representation of twists (nearly 8 thousand), abbreviations, slangs, rarely used words;
7. Division of vocabulary into 13 word-changing classes, which partially coincide with the
traditional parts of speech;
8. The dictionary does not contain accents;
9. Compact system of declension codes and tags for words;
10. Conjugation of complex names;
11. Replacement options for twists;
12. Information on case management;
13. Representation of homonyms.
The large electronic dictionary of the Ukrainian language is the basis for building a linguistic
analysis of texts of the Ukrainian language. It is already used to check spelling, grammar and style,
construct word vectors, implement a full-text search, and create a corpus. In addition, it can be used for
compiling various types of dictionaries, linguistic research, development in the field of computational
linguistics, reference functions, etc.
2.3. Microservices lang-uk
Microservices lang-uk can efficiently run and use the essential tools developed by the lang-uk team
[20]. Technically, this is implemented using Swagger and Docker technologies.
At the moment, there are the following services:
• Tokenisation;
• Ukrainian, Russian and English NER - allows NER-marking of token text using models trained
with the MITIE library for Ukrainian, Russian and English. The microservice was developed by
Mykhailo Chaly;
• Lematization – using the capabilities of the nlp-uk library, it is based on the NLP_UK library.
It allows lemmatising the input text according to the dict_uk dictionary, including its
tokenisation. The microservice was developed by Andriy Rysin;
• Language recognition using the capabilities of the WILD library - allows you to recognise the
language of input text from a list of 156 languages used on the Internet, using the library wiki-
lang-detect.
The lang-uk-ms project allows you to run all microservices simultaneously and access them through
a web interface. The web service (Docker image) was created to carry out such operations of processing
Ukrainian texts as an assessment of coherence of the text (the size of the text should make at least three
sentences); selection of nominal groups; search for co-reference pairs.
2.4. Corpus of NER-annotations
The corpus of NER-annotations contains 229 texts from the Ukrainian Brownian corpus for 217,381
tokens out of 6,751 marked named entities [18]. NER is a name that indicates a unique entity. These
include names of persons, places, organisations, works, websites, etc. It consists of one or more words.
Any NER entity must contain at least one word with a capital letter or be written in another language
(exceptions are the cases with an error, or the entire text is reduced to one register). But some entities
may also contain lowercase words [19]. Total in the case: 229 texts; 217381 tokens; 6751 NER entities.
2.5. Tonal dictionary of the Ukrainian language
The tonal dictionary of the Ukrainian language contains 3442 words of the Ukrainian language,
which have a non-neutral tone (-2, -1, 1, 2) [17]. Data are obtained from two sources:
• The file tone-dict-uk-manual.tsv is obtained by averaging the assessments of several experts;
• The tone-dict-uk-auto.tsv file is generated by automatically expanding the tone-dict-uk-manual
dictionary using the ML model applying the word vectors word2vec and lex2vec and minor
post-processing by humans.
The data format is tab-separated with the following columns - word and discrete key (from the range:
-2, -1, 0, 1, 2). If possible, all words are reduced to the basic grammatical form in the dictionary, and
common root adjectives replace adverbs.
3. Materials and Methods
3.1. UML-diagrams
UML diagrams are constructed for the selected topic, namely use-case, states and activities charts
are created. Now let’s take a closer look at each diagram separately. The diagram of variants of use is
presented in Fig. 1. It contains the following actors:
• User – a person who uses the system to analyse the Ukrainian text;
• VESUM database – a large electronic dictionary of the Ukrainian language, which contains word
changes of the Ukrainian language, the register, the code of word change classes and the rules
for generating word forms based on these codes;
• System database – a created database for the developed system.
Then this diagram contains the following use-cases:
• Analyse text - performs the primary function of the system. The user enters the text and begins
to analyse the entered text according to the following processes;
• Check the text language – checks the entered text (whether the text is written in Ukrainian;
• Tokenize – selects words from the entered text;
• Perform morphological analysis - a process that determines part of speech of all text words;
• Build sentence schemes – according to certain parts of speech it builds schemes of all sentences
in the entered text;
• Calculate the frequency of parts of speech in sentences – the frequency of occurrence of
grammatical conversion is calculated in each sentence separately, and then all the calculated
frequencies are summed;
• Calculate the frequency of combinations of grammatical conversion – the frequency of
combinations is calculated in each sentence separately, and then all the calculated frequencies
are summed;
• Save everything in the system database – all the results of the calculated frequencies and
constructed schemes are saved in the system database;
• Display the result of the analysis – displays the obtained result, namely graphs of frequencies of
parts of speech and frequencies of word combinations of grammatical conversion, as well as the
most common sentence schemes in the text;
• Build graphs – designs graphs of frequency occurrence of speech parts and frequencies of word
combinations of different parts of speech based on obtained data from the system database;
• Get frequency results – send a query to the database to get the calculated frequencies of different
parts of speech and frequencies of word combinations of other parts of speech;
• Display sentence schemes – identifies the most commonly used sentence schemes, which are
built based on sentences from the entered text;
• Get constructed diagrams – send a query to the database to obtain created diagrams of the text
sentences.
The state diagram is presented in Fig. 2. This diagram shows the transition of the system from one
state to another. The activity diagram is illustrated in Fig. 3. This diagram shows the user’s transition
from one activity to another when using the system.
Figure 1: Use-case diagram
Figure 2: State diagram
Figure 3: Activity diagram
3.2. Description of the basic functionality
First, the user sees the program window with empty fields. There is a field for entering text
and a button to start the analysis of the text. If the text is not entered in Ukrainian, the program
clears the field, notifies the user of the error, and asks him to enter the text again. Immediately
after the analysis, the bottom panel displays two frequency graphs and diagrams of the most
commonly used sentence schemes. Next, if the user continues the text analysis by entering the
new text and pressing the button, the bottom panel is cleared and filled with new graphs and
diagrams. And so on until the user finishes work and closes the program window. It should be
noted that all the analysis results are stored in the system database.
3.3. Means of implementation
The system is developed using the following tools:
• UI elements (Tkinter);
• MySQL Database;
• Back-end (Python).
4. Experiments
The structure of the software contains the following files:
• database.py contains all the necessary arrays of letters for morphological analysis;
• wordInfo.py has functions for obtaining the basic morphological information (part of speech,
word form and its case);
• tableMorphAnalysis.py includes operations for creating a graphical interface with
morphological analysis of words from the text;
• wordFrequency.py contains functions for subtracting the frequencies of different parts of speech
in the entered text and building graphs for them.
5. Results
A developed program analyses words of different parts of speech in Ukrainian texts. The program
builds sentence schemes, which represent the sequence of used parts of speech and displays a list of
frequencies of occurrence of speech part in the text. Below you can find a Ukrainian text with an
explanation of how the programme analyses it:
First, you enter the text in the field and then wait for the programme to start. The execution time
might be long, as the program has to process an extensive number of words and build appropriate
graphics. The results of the text processed by the program are presented in Figs. 4-7.
Figure 4: Sentence schemes and the word frequency
Figure 5: Morphological analysis
Figure 6: Frequency of different parts of speech used in the text
Figure 7: Frequency of different parts of speech used in sentences
Based on the conducted analysis, we may assume that a noun has the most significant frequency of
use. It means that the nouns take the most remarkable occurrence in sentences compared with other
words. The frequency is about 0.4 for the fraction and the adjective. The author gives additional
semantic nuances to individual words or sentences using fractions, and also, he provides other features
to certain words using adjectives. Thus, verbs and prepositions are present in the text, showing the
lowest frequency. Prepositions indicate relationships between different words in a sentence and
combine them. Therefore, it can be concluded that the author does not make many combinations of
words using prepositions. The verbs take the least number of occurrences indicating that the author does
not emphasise the performance of any action. We should also mention that some words are not
recognised to belong to any part of speech.
6. Discussion
We have chosen five texts of different styles to test the program’s analytics: publicistic, belles-
lettres, scientific, official, and conversational styles. The program calculates the frequency of parts of
speech throughout the text and separately for each sentence. In its final stage, the results of all texts are
compared. Diagrams for a publicistic text are presented in Fig. 8-9:
Figure 8: Frequency of different parts of speech in a publicistic text
Figure 9: Frequency of parts of speech used in sentences: publicistic text
Below you can find diagrams of the text in a belles-lettres style (see Fig. 10-11):
Figure 10: Frequency of different parts of speech in a belles-lettres text
Figure 11: Frequency of different parts of speech used in sentences: belles-lettres style
Another style which is considered is a scientific one. The text in a scientific style is given below and
the diagrams for it are presented in Fig. 12-13:
Figure 12: Diagram of the frequency of different parts of speech in a scientific text
Figure 13: Diagrams of the frequency of parts of speech used in sentences of a scientific text
The next text represents an official style and the diagrams for the frequency of used parts of speech
are presented in Fig. 14 - 15:
Figure 14: Diagram of the frequency of different parts of speech in an official text
Figure 15: Diagrams of the frequency of parts of speech used in sentences of the official text
The conversational style is represented in a text below. The diagrams of it are shown in Fig. 16-17:
Figure 16: Diagrams of frequency of different parts of speech in a conversational text
Figure 17: Diagrams of the frequency of parts of speech used in sentences: conversational style
Having analysed the texts with the application of the program functionality, we built a line diagram
that shows the frequency of different parts of speech used in texts of different styles (Fig. 18). Its results
demonstrate that the text of the conversational style has the highest number of the used parts of speech.
In other texts, the fluctuations between the frequencies of occurrence are approximately the same. It is
easy to recognise that verbs have the most significant frequency of use in the conversational text (a
conversation between two people discussing their further actions). Texts of other styles have the lowest
number of verb use compared with conversational text. Instead, other parts of speech such as nouns,
adjectives and prepositions are often used. The frequency of verbs, numerals, and adverbs in these texts
is about 0.
Figure 18: Frequency of parts of speech used in texts of different styles
7. Conclusions
The literature sources related to the morphological analysis of Ukrainian words and the frequency
use of different parts of speech in Ukrainian texts are considered in our research. We have investigated
the available analogue systems and applied the following: the intelligent system of Ukrainian text
processing, large electronic dictionary of the Ukrainian language (VESUM), microservices lang-uk,
NER-Annotations corpus and tonal dictionary of the Ukrainian language. The analysis of their features
and characteristics and their advantages and disadvantages is conducted in detail. It proves the relevance
and topicality of the project, which is to analyse the use of words of different parts of speech in
Ukrainian texts. A system analysis is chosen to perform our research. We constructed UML diagrams
to represent the general purpose of the developed system: use-case, states and activities. Each diagram
shows the system from different aspects to better understand its processes. In addition, the basic
functionality of the system is presented. As the implementation tools for creating a program, which will
analyse Ukrainian texts, the Python programming language, MySQL database, and Tkinter graphical
interface were chosen.
A program that analyses the use of words of different parts of speech in Ukrainian texts was
developed. The program builds sentence schemes, which represent the sequence of used parts of speech
and displays a list of frequencies of occurrence of parts of speech in the text. In addition, it displays
frequency graphs for the text and separately for each sentence and presents a morphological analysis of
each word in the entered text. Then, a control example of the developed program is shown; each element
of the program is described, and the entered text analysis is presented. The program has a convenient
and simple interface, is easy to use, and contains all design requirements. Accordingly, it displays all
the information about the word and the calculated frequency that it has processed.
We analysed the obtained results, namely the frequencies of using words of different parts of speech
in the texts of publicistic, belles-lettres, scientific, official, and conversational styles. It can be
concluded that the developed algorithms for morphological analysis, based on the rules of the Ukrainian
language, contain many inaccuracies as only a small number of exceptions for many Ukrainian words
are taken into account. In addition, it should also be noted that an algorithm for calculating the word
combinations frequencies of different parts of speech has not been developed. It means that the program
needs further improvement, building better algorithms and methods, and designing a coherent structure.
It also shows that the recognition of the Ukrainian language is a complex process and requires more
research, which provides a good starting point for discussion and further investigation.
8. References
[1] S. L. Holoshchuk, Istorychni peredumovy rozvytku korpusnoyi linhvistyky [Historical
preconditions for the development of corpus linguistics], International Academy Journal: Web of
Scholar 6 (15) (2017) 80-84.
[2] V. Lytvyn, V. Vysotska, P. Pukach, I. Bobyk, D. Uhryn, Development of a method for the
recognition of author’s style in the Ukrainian language texts based on linguometry, stylemetry and
glottochronology, Eastern-European Journal of Enterprise Technologies 4(2-88) (2017) 10-19.
doi: 10.15587/1729-4061.2017.107512.
[3] V. Vysotska, O. Kanishcheva, Y. Hlavcheva, Authorship Identification of the Scientific Text in
Ukrainian with Using the Lingvometry Methods, in: Proceedings of the International Conference
on Computer Sciences and Information Technologies, CSIT, 2018, pp. 34-38. doi: 10.1109/STC-
CSIT.2018.8526735.
[4] V. Vysotska, V.B. Fernandes, V. Lytvyn, M. Emmerich, M. Hrendus, Method for Determining
Linguometric Coefficient Dynamics of Ukrainian Text Content Authorship, Advances in
Intelligent Systems and Computing 871 (2019) 132-151. doi: 10.1007/978-3-030-01069-0_10.
[5] V. Vysotska, Ukrainian Participles Formation by the Generative Grammars Use, CEUR workshop
proceedings Vol-2604 (2020) 407-427.
[6] V. Vysotska, S. Holoshchuk, R. Holoshchuk, A comparative analysis for English and Ukrainian
texts processing based on semantics and syntax approach, CEUR Workshop Proceedings Vol-2870
(2021) 311-356.
[7] K. Tymoshenko, V. Vysotska, O. Kovtun, R. Holoshchuk, S. Holoshchuk, Real-time Ukrainian
text recognition and voicing, CEUR Workshop Proceedings Vol-2870 (2021) 357-387.
[8] A. Dmytriv, V. Vysotska, M. Bublyk, The Speech Parts Identification for Ukrainian Words Based
on VESUM and Horokh Using, in: Proceedings of the IEEE 16th International Conference on
Computer Sciences and Information Technologies (CSIT) :, 22-25 Sept., Lviv, Ukraine, 2021, Vol.
2, pp. 21–33. doi: 10.1109/CSIT52700.2021.9648813.
[9] L. Savytska, N. Vnukova, I. Bezugla, V. Pyvovarov, M. Turgut Sübay, Using Word2vec
Technique to Determine Semantic and Morphologic Similarity in Embedded Words of the
Ukrainian Language, CEUR Workshop Proceedings Vol-2870 (2021) 235-248.
[10] M. Sazhok, A. Poltieva, V. Robeiko, R. Seliukh, D. Fedoryn, Punctuation Restoration for
Ukrainian Broadcast Speech Recognition System based on Bidirectional Recurrent Neural
Network and Word Embeddings, CEUR Workshop Proceedings Vol-2870 (2021) 300-310.
[11] O. Cherednichenko, O. Kanishcheva, Readability Evaluation for Ukrainian Medicine Corpus
(UKRMED), CEUR Workshop Proceedings Vol-2870 (2021) 402-412.
[12] A. Luchyk, O. Taran, O. Palchevska, N. Sharmanova, G. Demydenko, Corpus-Driven Approaсh
to Ukrainian Е-Anecdotes Study, CEUR Workshop Proceedings Vol-2870 (2021) 424-434.
[13] V. Starko, Implementing Semantic Annotation in a Ukrainian Corpus, CEUR Workshop
Proceedings Vol-2870 (2021) 435-447.
[14] Н. Sytar, O. Vietrov, V. Diachenko, Synonymizer of the Ukrainian Language: Stage of Creation,
Features of Database Update and Software Implementation, CEUR Workshop Proceedings Vol-
2870 (2021) 448-458.
[15] N. Tmienova, B. Sus, System of Intellectual Ukrainian Language Processing, CEUR Workshop
Proceedings Vol-2577 (2019) 199-209. URL: http://ceur-ws.org/Vol-2577/paper16.pdf.
[16] Large Electronic Dictionary of the Ukrainian Language (VESUM) as a tool of NLP. URL:
https://www.researchgate.net/publication/344842033_Velikij_elektronnij_slovnik_ukrainskoi_m
ovi_VESUM_ak_zasib_NLP_dla_ukrainskoi_movi_Galaktika_Slova_Galini_Makarivni_Gnatuk
[17] Ukrainian tonal dictionary. URL: https://github.com/lang-uk/tone-dict-uk.
[18] Brown Corps of the Ukrainian language. URL: https://github.com/brown-uk/corpus.
[19] NER-text markup. URL: https://github.com/lang-uk/ner-uk/blob/master/doc/README.md.
[20] Microservices lang-uk. URL: https://lang.org.ua/uk/services/.
[21] V. Vysotska, Linguistic Analysis of Textual Commercial Content for Information Resources
Processing, in: Proceedings of the Modern Problems of Radio Engineering, Telecommunications
and Computer Science, TCSET, 2016, pp. 709-713. doi: 10.1109/TCSET.2016.7452160.
[22] P. Zhezhnych, A. Shilinh, V. Melnyk, Linguistic analysis of user motivations of information
content for university entrant’s web-forum, International Journal of Computing 18 (2019) 67-74.
[23] Lytvyn Vasyl, Vysotska Victoria, Dosyn Dmytro, Holoschuk Roman, Rybchak Zoriana,
Application of Sentence Parsing for Determining Keywords in Ukrainian Texts, in: Proceedings
of the International Conference on Computer Sciences and Information Technologies, CSIT, 2017,
pp. 326-331. doi: 10.1109/STC-CSIT.2017.8098797.
[24] Y. Burov, V. Vysotska, P. Kravets, Ontological approach to plot analysis and modeling, CEUR
Workshop Proceedings Vol-2362 (2019) 22-31.
[25] V. Lytvyn, V. Vysotska, O. Veres, I. Rishnyak, H. Rishnyak, Content linguistic analysis methods
for textual documents classification, in: Proceedings of the International Conference on Computer
Sciences and Information Technologies, CSIT, 2016, pp. 190-192. doi: 10.1109/STC-
CSIT.2016.7589903.
[26] O. Bisikalo, V. Vysotska, Linguistic analysis method of Ukrainian commercial textual content for
data mining, CEUR Workshop Proceedings Vol-2608 (2020) 224-244.
[27] V. Vysotska, V. Lytvyn, V. Kovalchuk, S. Kubinska, M. Dilai, B. Rusyn, L. Pohreliuk, L. Chyrun,
S. Chyrun, O. Brodyak, Method of Similar Textual Content Selection Based on Thematic
Information Retrieval, in: Proceedings of the International Conference on Computer Sciences and
Information Technologies, CSIT, 2019, pp. 1-6. doi: 10.1109/STC-CSIT.2019.8929752.
[28] V. Lytvyn, V. Vysotska, I. Peleshchak, T. Basyuk, V. Kovalchuk, S. Kubinska, L. Chyrun, B.
Rusyn, L. Pohreliuk, T. Salo, Identifying Textual Content Based on Thematic Analysis of Similar
Texts in Big Data, in: Proceedings of the International Conference on Computer Sciences and
Information Technologies, CSIT, 2019, pp. 84-91. doi: 10.1109/STC-CSIT.2019.8929808.
[29] S. Kubinska, V. Vysotska, Y. Matseliukh, User Mood Recognition and Further Dialog Support,
in: Proceedings of the IEEE 16th International Conference on Computer Sciences and Information
Technologies (CSIT), Lviv, 2021, vol. 2, pp. 34–39. doi: 10.1109/CSIT52700.2021.9648610.
[30] V. Lytvyn, N. Sharonova, T. Hamon, V. Vysotska, N. Grabar, A. Kowalska-Styczen,
Computational linguistics and intelligent systems, CEUR Workshop Proceedings 2136 (2018).
[31] V. Lytvyn, N. Sharonova, T. Hamon, O. Cherednichenko, N. Grabar, A. Kowalska-Styczen, V.
Vysotska, Preface, CEUR Workshop Proceedings Vol-2362 (2019).
[32] V. Lytvyn, V. Vysotska, T. Hamon, N. Grabar, N. Sharonova, O. Cherednichenko, O.
Kanishcheva, Preface, CEUR Workshop Proceedings Vol-2604 (2020).
[33] N. Sharonova, V. Lytvyn, O. Cherednichenko, Y. Kupriianov, O. Kanishcheva, T. Hamon, N.
Grabar, V. Vysotska, A. Kowalska-Styczen, I. Jonek-Kowalska, Preface, CEUR Workshop
Proceedings Vol-2870 (2021).
[34] V. Husak, O. Lozynska, I. Karpov, I. Peleshchak, S. Chyrun, A. Vysotskyi, Information System
for Recommendation List Formation of Clothes Style Image Selection According to User’s Needs
Based on NLP and Chatbots, CEUR workshop proceedings Vol-2604 (2020) 788-818.
[35] O. Romanovskyi, N. Pidbutska, A. Knysh, Elomia Chatbot: The Effectiveness of Artificial
Intelligence in the Fight for Mental Health, CEUR Workshop Proceedings 2870 (2021) 1215-1224.
[36] A. Yarovyi, D. Kudriavtsev, Method of Multi-Purpose Text Analysis Based on a Combination of
Knowledge Bases for Intelligent Chatbot, CEUR Workshop Proceedings 2870 (2021) 1238-1248.
[37] N. Shakhovska, O. Basystiuk, K. Shakhovska, Development of the Speech-to-Text Chatbot
Interface Based on Google API, CEUR Workshop Proceedings Vol-2386 (2019) 212-221.
[38] D. Aksonov, A. Gozhyj, I. Kalinina, V. Vysotska, Question-Answering Systems Development
Based on Big Data Analysis, in: Proceedings of the IEEE 16th International Conference on
Computer Sciences and Information Technologies (CSIT), 22-25 Sept., Lviv, Ukraine, 2021, Vol.
1. pp. 113–118. doi: 10.1109/CSIT52700.2021.9648631.
[39] V. Lytvyn, V. Vysotska, A. Rzheuskyi, Technology for the Psychological Portraits Formation of
Social Networks Users for the IT Specialists Recruitment Based on Big Five, NLP and Big Data
Analysis, CEUR Workshop Proceedings Vol-2392 (2019) 147-171.
[40] J. Deriviere, T. Hamon, A. Nazarenko, A scalable and distributed NLP architecture for web
document annotation, Lecture Notes in Computer Science (including subseries Lecture Notes in
Artificial Intelligence and Lecture Notes in Bioinformatics) 4139 (2006) 56–67.
[41] M. Boyè, T. M. Tran, N. Grabar, NLP-oriented contrastive study of linguistic productions of
alzheimer’s and control people, Lecture Notes in Computer Science (including subseries Lecture
Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8686 (2014) 412–424.
[42] C. Shu, D. Dosyn, V. Lytvyn, V. Vysotska, A. Sachenko, S. Jun, Building of the Predicate
Recognition System for the NLP Ontology Learning Module, in: Proceedings of the International
Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and
Applications, IDAACS, 2, 2019, pp. 802-808. doi: 10.1109/IDAACS.2019.8924410.
[43] V.-A. Oliinyk, V. Vysotska, Y. Burov, K. Mykich, V. Basto-Fernandes, Propaganda Detection in
Text Data Based on NLP and Machine Learning, CEUR workshop proceedings Vol-2631 (2020)
132-144.
[44] I. Balush, V. Vysotska, S. Albota, Recommendation System Development Based on Intelligent
Search, NLP and Machine Learning Methods, CEUR Workshop Proceedings Vol-2917 (2021)
584-617.
[45] T. Batura, A. Bakiyeva, M. Charintseva, A method for automatic text summarization based on
rhetorical analysis and topic modeling, International Journal of Computing 19(1) (2020) 118-127.
[46] H. Schöpper, W. Kersten, Using Natural Language Processing for Supply Chain Mapping: a
Systematic Review of Current Approaches, CEUR Workshop Proceedings 2870 (2021) 71-86.
[47] M. Zanchak, V. Vysotska, S. Albota, The Sarcasm Detection in News Headlines Based on Machine
Learning Technology, in: Proceedings of the IEEE 16th International Conference on Computer
Sciences and Information Technologies (CSIT), 22-25 Sept., Lviv, Ukraine, 2021, Vol. 1. pp. 131–
137. doi: 10.1109/CSIT52700.2021.9648710.
[48] N. Kholodna, V. Vysotska, S. Albota, A Machine Learning Model for Automatic Emotion
Detection from Speech, CEUR Workshop Proceedings Vol-2917 (2021) 699-713.
[49] D. Nazarenko, I. Afanasieva, N. Golian, V. Golian, Investigation of the Deep Learning Approaches
to Classify Emotions in Texts, CEUR Workshop Proceedings Vol-2870 (2021) 206-224.
[50] I. Bekhta, N. Hrytsiv, Computational Linguistics Tools in Mapping Emotional Dislocation of
Translated Fiction, CEUR Workshop Proceedings Vol-2870 (2021) 685-699.
[51] I. Spivak, S. Krepych, O. Fedorov, S. Spivak, Approach to Recognizing of Visualized Human
Emotions for Marketing Decision Making Systems, CEUR Workshop Proceedings Vol-2870
(2021) 1292-1301.
[52] P. C. Thoumelin, N. Grabar, Subjectivity in the medical discourse: On uncertainty and emotional
markers [La subjectivité dans le discours médical: Sur les traces de l'incertitude et des émotions],
Revue des Nouvelles Technologies de l'Information E.26 (2014) 455–466.
[53] N. Grabar, L.O. Dumonet, Automatic computing of global emotional polarity in French health
forum messages, Lecture Notes in Computer Science (including subseries Lecture Notes in
Artificial Intelligence and Lecture Notes in Bioinformatics) 9105 (2015) 243–248.
[54] Z. Kochuieva, N. Borysova, K. Melnyk, D. Huliieva, Usage of Sentiment Analysis to Tracking
Public Opinion, CEUR Workshop Proceedings Vol-2870 (2021) 272-285.
[55] N. Bondarchuk, I. Bekhta, Quantitative Characteristics of Lexical-Semantic Groups Representing
Weather in Weather News Stories (Based on British Online Press), CEUR Workshop Proceedings
Vol-2870 (2021) 799-810.
[56] O. Artemenko, V. Pasichnyk, N. Kunanets, K. Shunevych, Using sentiment text analysis of user
reviews in social media for e-tourism mobile recommender systems, CEUR workshop proceedings
Vol-2604 (2020) 259-271.
[57] V. Bobicev, O. Kanishcheva, O. Cherednichenko, Sentiment Analysis in the Ukrainian and
Russian News, in: First Ukraine Conference on Electrical and Computer Engineering (UKRCON),
2017 pp. 1050-1055.
[58] S. Bhatia, M. Sharma, K. K. Bhatia, P. Das. Opinion target extraction with sentiment analysis,
International Journal of Computing 17(3) (2018) 136-142.
[59] K. Shakhovska, N. Shakhovska, P. Veselý, The sentiment analysis model of services providers’
feedback, Electronics (Switzerland) 9(11) (2020) 1–15.
[60] V. Turchenko, L. Grandinetti, A. Sachenko, Parallel batch pattern training of neural networks on
computational clusters, in: Proceedings of the International Conference on High Performance
Computing & Simulation (HPCS), 2012, pp. 202-208, doi: 10.1109/HPCSim.2012.6266912.
[61] P. Kossakowski, P. Bilski, Analysis of the self-organizing map-based investment strategy,
International Journal of Computing 16(1) (2017) 10-17.
[62] M. Maree, M. Eleyat, Semantic graph based term expansion for sentence-level sentiment analysis,
International Journal of Computing 19(4) (2020) 647-655.
[63] S. Shrivastava, K. V. Lakshmy, C. Srinivasan, On the Statistical Analysis of ZUC, Espresso and
Grain v1, International Journal of Computing 20(3) (2021) 384-390.
[64] V. Turchenko, V. A. Golovko, A. Sachenko, Parallel Batch Pattern Training of Recirculation
Neural Network, in: Proceedings of the 9th International Conference on Informatics in Control,
Automation and Robotics, ICINCO, 2012, 1, pp. 644–650.
[65] I. Paliy, A. Sachenko, Y. Kurylyak, O. Boumbarov, S. Sokolov, Combined approach to face
detection for biometric identification systems, in: Proceedings of the IEEE International Workshop
on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications,
2009, pp. 425-429. doi: 10.1109/IDAACS.2009.5342946.
[66] Md Maksudur R. Mazumder, C. Phillips, Partitioning known environments for multi-robot task
allocation using genetic algorithms, International Journal of Computing 19(3) (2020) 480-490.
[67] M. Patil, T. Abukhalil, S. Patel, T. Sobh, UB SWARM: hardware implementation of heterogeneous
swarm robot with fault detection and power management, International Journal of Computing
15(3) (2016) 162-176.
[68] V. M. Hung, V. Mihai, C. Dragana, I. Ion, N. Paraschiv, Dynamic computation of haptic-robot
devices for control of a surgical training system, International Journal of Computing 17(2) (2018)
81-93.