Analysis of Lexical Semantic Changes in Corpora with the Diachronic Engine
Authors: Pierluigi Cassotti, Pierpaolo Basile, Marco de Gemmis, Giovanni Semeraro (Università di Bari Aldo Moro, 2020)
Topic: Latin Resources

Abstract
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 time-series that can be exploited for lexical semantic change detection.

Introduction
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 Engine (Kilgarriff et al., 2004; Kilgarriff et al., 2014) is the leading tool in the corpus analysis field. Beyond several interesting features, Sketch Engine includes trends (Kilgarriff et al., 2015), which allow for diachronic analysis based on the frequency distribution of words. Trends rely on merely frequency features, ignoring word usage information. Moreover, the Sketch Engine interface does not provide temporal information about concordances and collocations. NoSketchEngine is an open-source version of SketchEngine. It requires technical expertise for the setup and, contrarily to SketchEngine, it does not support word sketches, terminology, thesaurus, n-grams, trends and corpus building. An interesting system is DiaCollo (Jurish and der Wissenschaften, 2015), a software tool for the discovery, comparison, and interactive visualization of target word combinations. Combinations can be requested for a particular time period, or for a direct comparison between different time periods. However, DiaCollo is focused exclusively on the extraction and visualization of collocations from diachronic corpora. In recent works about computational diachronic linguistics, techniques based on word embeddings produce promising results. In Semeval Task 1 (Schlechtweg et al., 2020), for instance, type embeddings rich high performances on both subtasks. However, these techniques are not included in any aforementioned linguistic tool. In order to bridge this gap, we try to build a tool that includes approaches for the analysis of diachronic embeddings. The result of our work is Diachronic Engine (DE), an engine for the management of diachronic corpora that provides tools for change detection of lexical semantics from a frequentist perspective. DE includes tools for extracting diachronic collocations, concordances in different time periods as well as for computing semantic change time-series by exploiting both word frequencies and word embeddings similarity over time. The rest of the paper is organized as follows: 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.

Conclusion
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 (Onelli et al., 2006) and the integration of other approaches for computing diachronic word embeddings. Moreover, we plan to add a tool for the annotation of lexical-semantic shifts inspired by DUREL (Schlechtweg et al., 2018).