English. With the growing availability of digitized diachronic corpora, the need for tools capable of taking into account the diachronic component of corpora becomes ever more pressing. Recent works on diachronic embeddings show that computational approaches to the diachronic analysis of language seem to be promising, but they are not user friendly for people without a technical background. This paper presents the Diachronic Engine, a system for the diachronic analysis of corpora lexical features. Diachronic Engine computes word frequency, concordances and collocations taking into account the temporal dimension. It is also able to compute temporal word embeddings and timeseries that can be exploited for lexical semantic change detection.
Synchronic corpora are widely used in
linguistics for deriving a set of abstract rules that
govern a particular language under analysis by
using statistical approaches. The same
methodology can be adopted for analyzing the evolution
of word meanings over time in the case of
diachronic corpora. However, this process can be
very time-consuming. Usually, linguists rely on
software tools that can easily explore and clean the
corpus, while highlighting the more relevant
linguistic features. Sketch Engine1
In recent works about computational diachronic
linguistics, techniques based on word embeddings
produce promising results. In Semeval Task 1
The rest of the paper is organized as follows: 2https://nlp.fi.muni.cz/trac/noske 3https://www.clarin.eu/showcase/ diacollo Section 2 describes the technical details of DE, while Section 3 shows some use cases of our engine that encompass that address time-series. We also present the results of a preliminary evaluation about the system’s usability in Section 4. Conclusions and future work close the paper. 2
Diachronic Engine (DE) is a web application for lexical semantic change analysis in diachronic corpora. The DE pipeline needs diachronic corpora to compute statistics about the corpus. A diachronic corpus must include a temporal feature (e.g., year or timestamp of the publication date); DE exploits that feature to sort the documents.
We adopt the vertical format to represent word information, as specified for the IMS Corpus Workbench (CWB). In a vertical corpus, each word is in a new line. In each line, fields, called p-attributes, are separated by tabs. In DE the default p-attributes are word, lemma, PoS tag and syntactic dependency. Non-recursive XML tags (s-attributes) on a separate line can be used for representing sentences, paragraphs and documents.
Corpora can be served in vertical format4 or in
plain-text mode; in the latter case, the plain-text
is transformed in vertical format using the Spacy
UDPipe5
5https://pypi.org/project/ spacy-udpipe/ 6http://universaldependencies.org terns in the name of the files. In particular, the last sequence of numbers in the file name is used to sort the documents.
Corpora are stored and managed by the CWB,
a tool for the manipulation of large, linguistically
annotated corpora. In particular, DE relies on
the Corpus Query Processor (CQP)
For building temporal word embeddings, DE
exploits Temporal Random Indexing (TRI)
Future versions will include other approaches,
such as Procustes
The DE architecture is based on the clientserver paradigm. The back-end of DE has been developed with Flask, a web framework written in Python. Concordances are retrieved by CQP, that indexes the corpus as soon as it is uploaded to the server, while collocations and frequencies are computed in Python. The back-end provides a set of services by a REST API where the input/output is based on JSON messages.
The back-end consists of three macro components: User Handler, Corpus Handler and Diachronic Operations. The User Handler manages registered users information such as username and passwords. Admitted operations on users are creation, read, update and delete. The Corpus Handler Component manages corpora information such as name, language, the list of fields in the vertical files, corpus visibility. Moreover, it deals with corpora types: each corpus has a label indicating if it is synchronic or diachronic. For diachronic corpora also the temporal range is stored. Operations admitted on corpora are: creation, update, delete, search and read. The Diachronic Operations component shows frequency lists, collocations of words, time-series, change-points and concordances. This component relies on CWB that indexes vertical files.
The Diachronic Operations component architecture is sketched in Figure 2.
The front-end of DE has been developed with JHipster7, using Spring8 for server-side applications and Angular for client-side applications. The front-end communicates with the back-end by the means of the REST API.
The front-end design is inspired by the Google’s Material Design and the Sketch Engine interface. The user interface provides multilingual support in Italian and English, but we plan to extend it to other languages.
This architecture allows the independence between the back-end and the front-end, in this way is possible to develop a different front-end or connect the front-end to a different implementation of the back-end. The only constraint is the REST API interface.
A screenshot of the DE homepage is provided in Figure 1. The homepage provides an easy access to all corpora owned by the logged user with links to available tools. The front-end provides also tools for creating and managing users and corpora. In particular, it is possible to define different grant permissions for each corpus.
The tool is distributed as open-source software
under the GNU v3 license9.
7https://www.jhipster.tech/
8https://spring.io/
9https://github.com/swapUniba/
Diachronic-Engine
DE provides a set of tools for managing and
querying diachronic corpora. The core of the
backend is based on the IMS Open Corpus Workbench
(CWB) 10, which allows querying the indexed
corpora by using the powerful CQP. Other tools have
been integrated to facilitate the analysis of a
diachronic corpus:
Word frequency Many works show a
correlation between lexical semantic change and
frequency differences between time periods.
Google Ngram Viewer
Collocations Collocations have shown to be an
effective tool in diachronic analysis
2fxy
fx + fy
logDice takes into account the frequency of
the word fx, of the collocate fy and the
frequency of the whole collocation fxy.
Collocation results can be grouped by the PoS tag.
Concordances Concordances offer a way to find
“the evidence” directly in the text by
exploiting the context. The Concordances tool lists
instances of a word with its immediate left
and right context and the period the
collocation belongs to. An example of concordances
from “L’Unita`”
Time-series A time-series (w) of a word w is an ordered sequence of cosine similarities between the word vector at time k (vwk) and the previous one at time k 1 (vwk 1): (w)k = vwk vwk 1 jvwkjjvwk 1j 10http://cwb.sourceforge.net/
Diachronic Engine relies on word vectors
computed by Temporal Random Indexing,
but it is possible to integrate other
approaches. In order to detect change points,
we use the Mean Shift algorithm
K( ) = l 1
l
X
j k=j+1
k
1 Xj
j k=1
k
(1)
In order to understand if a mean shift is
statistically significant at time j, a bootstrapping
In this section, we describe two use cases
concerning both historical and computational linguistics.
DE is an extension of existing tools for synchronic
corpora. It shares many of the use cases already
available on those tools, such as applications in
lexicography, terminology and linguistics.
Lexical semantic changes can reveal aspects of
real-world events, such us global armed conflicts
In Figure 4, the time-series of “terrorismo” (terrorism) is shown. The time-series appears to be influenced by real-world events happening in Italy. In particular, we can observe a decrease in similarity starting in 1968 and culminating in 1970 during a crucial moment in Italy: “Anni di piombo” (Years of Lead), years marked by terrorism and violent clashes carried out by political activists. 3.2
Annotation of semantic shifts
The manual annotation of lexical-semantic shifts
can be very expensive. Although robust
frameworks
DE can provide useful tools for the annotation
of semantic shifts:
1. Frequencies over time can be preliminary
exploited to filter words that have good
coverage in the years under analysis;
2. Change points in time-series offer an
overall and intuitive idea of the potential semantic
shifts;
3. Diachronic concordances and collocations
can support the identification of the type of
change
We place a particular focus on the usability of our
tool by giving a satisfactory experience. To
understand the strength and weakness of the user
interface, we conduct a preliminary usability test,
according to the eGLU protocol
Results of the evaluation are reported in Table 1.
User registration Login and show user information Add a corpus Show frequency list Overall
1 1
Moreover, we designed and dispensed a questionnaire for measuring user satisfaction. The questionnaire is composed of ten questions about the usability and the design of DE with a Likert scale of five values. The questionnaire results return an average score of 84.05/100. The system appear likeable to use. 5
In this paper, we present the Diachronic Engine, a tool for the analysis of lexical semantic change. DE integrates and extends current tools for corpus analysis enabling the study of corpus diachronic features. DE includes tools not included in other systems, such as time-series and change points detection based on diachronic word embeddings.
As future work, we plan to provide pre-loaded
corpora such as Google Ngram, Diacoris
The authors would like to thank Dr. Ferrante and
Dr. Lopatriello for supporting the preliminary
development of the Diachronic Engine