Lexical Semantic Change Research Articles

Sense through time: diachronic word sense annotations for word sense induction and Lexical Semantic Change Detection

AbstractThere has been extensive work on human word sense annotation, i.e., manually labeling word uses in natural texts according to their senses. Such labels were primarily created for the tasks of Word Sense Disambiguation (WSD) and Word Sense Induction (WSI). However, almost all datasets annotated with word senses are synchronic datasets, i.e., contain texts created in a relatively short period of time and often do not provide the creation date of the texts. This ignores possible applications in diachronic-historic settings, where the aim is to induce or disambiguate historical word senses or changes in senses across time. To facilitate investigations into historical WSD and WSI and to establish connections with the task of Lexical Semantic Change Detection (LSCD), there is a crucial need for historical word sense-annotated data. Hence, we created a new reliable diachronic WSD/WSI dataset ‘DWUG DE Sense’. We describe the preparation and annotation and analyze central statistics. We then describe a thorough evaluation of different prediction systems for jointly solving both WSI and LSCD tasks. All our systems are based on a state-of-the-art architecture that combines Word-in-Context models and graph clustering techniques with different hyperparameter settings. Our findings reveal that using the WSI task as optimization criterion yields better results for both tasks even when the LSCD task is the focal point of optimization. This underscores that although both tasks are related, WSI seems to be more general and able to incorporate the LSCD task.

Lexical Semantic Change through Large Language Models: a Survey

Lexical Semantic Change (LSC) is the task of identifying, interpreting, and assessing the possible change over time in the meanings of a target word. Traditionally, LSC has been addressed by linguists and social scientists through manual and time-consuming analyses, which have thus been limited in terms of the volume, genres, and time-frame that can be considered. In recent years, computational approaches based on Natural Language Processing have gained increasing attention to automate LSC as much as possible. Significant advancements have been made by relying on Large Language Models (LLMs), which can handle the multiple usages of the words and better capture the related semantic change. In this article, we survey the approaches based on LLMs for LSC and we propose a classification framework characterized by three dimensions: meaning representation , time-awareness , and learning modality . The framework is exploited to i) review the measures for change assessment, ii) compare the approaches on performance, and iii) discuss the current issues in terms of scalability, interpretability, and robustness. Open challenges and future research directions about the use of LLMs for LSC are finally outlined.

Evaluation of word embedding models used for diachronic semantic change analysis

In the last decade, the quantitative analysis of diachronic changes in language and lexical semantic changes have become the subject of active research. A significant role was played by the development of new effective techniques of word embedding. This direction has been effectively demonstrated in a number of studies. Some of them have focused on the analysis of the optimal type of word2vec models, hyperparameters for training, and evaluation techniques. In this research, we used Corpus of Historical American English (COHA). The paper demonstrates the results of multiple training runs and the comparison of word2vec models with different variations of hyperparameters used for lexical semantic change detection. In addition to traditional word similarities and analogical reasoning tests, we used testing on an extended set of synonyms. We have evaluated word2vec models on the set of more than 100,000 English synsets that were randomly selected from the WordNet database. We have shown that changing the word2vec model parameters (such as a dimension of word embedding, a size of context window, a type of model, a word discard rate etc.) can significantly impact on the resulting word embedding vector space and the detected lexical semantic changes. Additionally, the results strongly depended on properties of the corpus, such as word frequency distribution.

How Fast Do Distribution and Semantics of Polysemic Words Change?

Creation of large diachronic text corpora triggered studies of evolution of natural languages using quantitative methods in the last decade. For the first time, it became possible to raise questions about general patterns of lexical semantic change. In the work by W. Hamilton et al., 2016, a hypothesis was formulated about a more rapid change in the meanings of polysemic words. In this paper, we consider how frequency of words influence variations of semantic metrics. We use explicit word vector representations and Jensen-Shannon divergence as a measure of change in the distribution and semantics of a word. The obtained results show that changes in the frequency ratio of a word in different meanings can themselves lead to a change in a word distribution, even in the absence of any changes in its meaning. Therefore, the observations made in the work by W. Hamilton et al about correlation between the rate of change of words and their local cluster coefficient in the semantic connection network (considered as a quantitative measure of polysemy) can be fully explained by the effect described in the article.

Leksičko-semantička reprezentacija delova tela kod dece na srpskom jeziku

Although words for human body parts appear early in children's vocabulary, relatively little is known about the conceptual and semantic development related to the body part words in preschool and early school ages. In this paper, we examine how children at ages 5, 7 and 9 use words and expressions to refer to the human body and its parts, and how these correspond to the segmentation and lexicalization of the body part terms in adults. Participants were asked to name the body parts that were depicted in the drawings showing the whole body (front or back) and the face, with a red dot marking the specific part. The results of the comparison between children and adults indicate that for the most parts of arms, legs, and face there is a gradual conceptual segmentation of body with age, reflected in a decrease in the use of holonyms and an increase in the use of meronyms in naming parts. However, such hierarchical organization could not be confirmed for other parts (trunk, shoulders, neck, head, some parts of the face), revealing different pathways in the acquisition of words. Children of all ages, especially 7and 9-year-olds, seek alternative solutions for naming the body parts for which labels are missing in their vocabulary. In such cases, they name adjacent body parts, internal organs, and parts of the skeleton, or use prepositional phrases to refer to the surrounding areas. The results are compared with the findings of the previous studies, while the lexical-semantic change in the body parts terms and the hierarchical organization of the body part lexicon in child language are discussed. The results were compared in light of previous findings of the developmental studies, on the lexical-semantic change, and the hierarchical organization of the body part lexicon in child language.

Regularity of semantic change in Romance anatomical terms

Abstract While semantic change is notoriously idiosyncratic, cross-linguistic evidence suggests some general trends in the directions and mechanisms of semantic shifts. Notable among these are trends applying to the target domain of the human body, a domain that has received considerable attention due to its universality. However, broad surveys of many languages risk missing significant details. Data from the Dictionnaire étymologique et cognitif des langues romanes (DECOLAR) on the etymologies of terms for 97 body parts in 14 Romance languages calls some proposed trends into question. In particular, counterexamples are found to a supposed unidirectional shift from visible parts to the wholes that include them. Analysis of individual changes reveals contextual factors that can cause a lexical trend to not apply. The findings contribute to a more complex model of metaphorization and metonymization, the primary processes involved in lexical semantic change.

SenSE: A Toolkit for Semantic Change Exploration via Word Embedding Alignment

Lexical Semantic Change (LSC) detection, also known as Semantic Shift, is the process of identifying and characterizing variations in language usage across different scenarios such as time and domain. It allows us to track the evolution of word senses, as well as to understand the difference between the language used in distinct communities. LSC detection is often done by applying a distance measure over vectors of two aligned word embedding matrices. In this demonstration, we present SenSE, an interactive semantic shift exploration toolkit that provides visualization and explanation of lexical semantic change for an input pair of text sources. Our system focuses on showing how the different alignment strategies may affect the output of an LSC model as well as on explaining semantic change based on the neighbors of a chosen target word, while also extracting examples of sentences where these semantic deviations appear. The system runs as a web application (available at http://sense.mgruppi.me), allowing the audience to interact by configuring the alignment strategies while visualizing the results in a web browser.

Metonymy and argument alternations in French communication frames

AbstractThis study describes metonymic argument alternations, in which a constructional slot can be filled by any of a set of semantic roles that index one another, and provides a diachronic corpus analysis of two such alternations in French. In theReveal secretframe and other communication frames, the Medium can indexically replace the Speaker and the Topic can indexically replace the Information. A regression analysis shows that whiletopic for informationmetonymy is more syntactically and pragmatically restricted,medium for speakermetonymy has seen an increase in usage over time across the frame. This change is related to sociocultural developments and has implications for the study of figurative language and lexical semantic change. While it is well understood that figurative mechanisms such as metonymy affect language change, here it is demonstrated that cultural shifts drive changes in metonymies themselves, with corresponding impacts for linguistic structure.

Neural Network Algorithm for Detection of New Word Meanings Denoting Named Entities

Lexical semantic change detection has been a rapidly developing field of science in recent years. Existed algorithms of lexical semantic change detection face difficulties when they are used to work with words denoting named entities. This paper proposes a method that allows one to reveal a word in a large corpus that started being used as a named entity, as well as to date the first usage of this word as a proper name. To solve this problem, firstly, we offer an algorithm that allows for detecting words in a large corpus denoting named entities. The recognizer is based on an analysis of co-occurrences with the most frequent words and was trained on data from the English subcorpus of the Google Books Ngram corpus. The achieved recognition accuracy of named entities is 98.44% on the test sample. Secondly, we test the possibility of applying the trained recognizer to diachronic data. The analysed cases show that the recognizer initially trained using the total bigram frequencies for a long time interval, at least for any frequent word, provides stable results for the annual frequency values. This can make the recognizer a good tool for language evolution studies, especially for detecting new meanings of words. The analysed cases show that the proposed method allows revealing new word meanings associated with named entities, as well as detecting genericized meaning of words that were earlier used as proper names.

Of travels and travails: The role of semantic typology, argument structure constructions, and language contact in semantic change

Abstract This paper is an investigation into the role of argument structure constructions as catalysts or blockers of lexical semantic change. It presents a case study of the divergent semantic development of French travailler ‘work’ and English travel ‘journey’ from their shared earlier meaning ‘labour, toil’. This divergence is shown to not be random: It can be explained as a product of the different intransitive motion constructions (IMCs) and different communicative habits in these two languages. Consequently, the development of travailler ‘journey’ in the Anglo-Norman dialect of French can be understood as the result of contact influence of Middle English. By pointing to similar instances in which verbs meaning ‘labour, toil’ have acquired a polysemous ‘motion’ sense in languages with an IMC that can coerce non-motion verbs into contextual motion readings, the paper argues that this is most probably a regular semantic trajectory in satellite-framing, manner-conflating languages.

DUKweb, diachronic word representations from the UK Web Archive corpus

Lexical semantic change (detecting shifts in the meaning and usage of words) is an important task for social and cultural studies as well as for Natural Language Processing applications. Diachronic word embeddings (time-sensitive vector representations of words that preserve their meaning) have become the standard resource for this task. However, given the significant computational resources needed for their generation, very few resources exist that make diachronic word embeddings available to the scientific community. In this paper we present DUKweb, a set of large-scale resources designed for the diachronic analysis of contemporary English. DUKweb was created from the JISC UK Web Domain Dataset (1996–2013), a very large archive which collects resources from the Internet Archive that were hosted on domains ending in ‘.uk’. DUKweb consists of a series word co-occurrence matrices and two types of word embeddings for each year in the JISC UK Web Domain dataset. We show the reuse potential of DUKweb and its quality standards via a case study on word meaning change detection.

Lexical borrowing in the Middle English period: a multi-domain analysis of semantic outcomes

The Middle English period is well known as one of widespread lexical borrowing from French and Latin, and scholarly accounts traditionally assume that this influx of loanwords caused many native terms to shift in sense or to drop out of use entirely. The study analyses an extensive dataset, tracking patterns in lexical retention, replacement and semantic change, and comparing long-term outcomes for both native and non-native words. Our results challenge the conventional view of competition between existing terms and foreign incomers. They show that there were far fewer instances of relexification, and far more of synonymy, during the Middle English period than might have been expected. When retention rates for words first attested between 1100 and 1500 are compared, it is loanwords, not native terms, which are more likely to become obsolete at any point up to the nineteenth century. Furthermore, proportions of outcomes involving narrowing and broadening (often considered common outcomes following the arrival of a co-hyponym in a semantic space) were low in the Middle English period, regardless of language of origin.

Fake it Till You Make it: Self-Supervised Semantic Shifts for Monolingual Word Embedding Tasks

The use of language is subject to variation over time as well as across social groups and knowledge domains, leading to differences even in the monolingual scenario. Such variation in word usage is often called lexical semantic change (LSC). The goal of LSC is to characterize and quantify language variations with respect to word meaning, to measure how distinct two language sources are (that is, people or language models). Because there is hardly any data available for such a task, most solutions involve unsupervised methods to align two embeddings and predict semantic change with respect to a distance measure. To that end, we propose a self-supervised approach to model lexical semantic change based on the perturbation of word vectors in the input corpora. We show that our method can be used for the detection of semantic change with any alignment method. Furthermore, it can be used to choose the landmark words to use in alignment and can lead to substantial improvements over the existing techniques for alignment. We illustrate the utility of our techniques using experimental results on three different datasets, involving words with the same or different meanings. Our methods not only provide significant improvements but also can lead to novel findings for the LSC problem.

Detecting Lexical Semantic Change across Corpora with Smooth Manifolds (Student Abstract)

Comparing two bodies of text and detecting words with significant lexical semantic shift between them is an important part of digital humanities. Traditional approaches have relied on aligning the different embeddings using the Orthogonal Procrustes problem in the Euclidean space. This study presents a geometric framework that leverages smooth Riemannian manifolds for corpus-specific orthogonal rotations and a corpus-independent scaling metric to project the different vector spaces into a shared latent space. This enables us to capture any affine relationship between the embedding spaces while utilising the rich geometry of smooth manifolds.

Abolitionist Networks: Modeling Language Change in Nineteenth-Century Activist Newspapers

The abolitionist movement of the nineteenth-century United States remains among the most significant social and political movements in US history. Abolitionist newspapers played a crucial role in spreading information and shaping public opinion around a range of issues relating to the abolition of slavery. These newspapers also serve as a primary source of information about the movement for scholars today, resulting in powerful new accounts of the movement and its leaders. This paper supplements recent qualitative work on the role of women in abolition’s vanguard, as well as the role of the Black press, with a quantitative text modeling approach. Using diachronic word embeddings, we identify which newspapers tended to lead lexical semantic innovations — the introduction of new usages of specific words — and which newspapers tended to follow. We then aggregate the evidence across hundreds of changes into a weighted network with the newspapers as nodes; directed edge weights represent the frequency with which each newspaper led the other in the adoption of a lexical semantic change. Analysis of this network reveals pathways of lexical semantic influence, distinguishing leaders from followers, as well as others who stood apart from the semantic changes that swept through this period. More specifically, we find that two newspapers edited by women – The Provincial Freeman and The Lily – led a large number of semantic changes in our corpus, lending additional credence to the argument that a multiracial coalition of women led the abolitionist movement in terms of both thought and action. It also contributes additional complexity to the scholarship that has sought to tease apart the relation of the abolitionist movement to the women’s suffrage movement, and the vexed racial politics that characterized their relation.

Analyzing Gaussian distribution of semantic shifts in Lexical Semantic Change Models

In recent years, there has been a significant increase in interest in lexical semantic change detection. Many are the existing approaches, data used, and evaluation strategies to detect semantic shifts. The classification of change words against stable words requires thresholds to label the degree of semantic change. In this work, we compare state-of-the-art computational historical linguistics approaches to evaluate the efficacy of thresholds based on the Gaussian Distribution of semantic shifts. We present the results of an in-depth analysis conducted on both SemEval-2020 Task 1 Subtask 1 and DIACR-Ita tasks. Specifically, we compare Temporal Random Indexing, Temporal Referencing, Orthogonal Procrustes Alignment, Dynamic Word Embeddings and Temporal Word Embedding with a Compass. While results obtained with Gaussian thresholds achieve state-of-the-art performance in English, German, Swedish and Italian, they remain far from results obtained using the optimal threshold.

On the Problem of Lexical Semantic Change

The article provides an insight into a problem of lexical semantic change. A short historical outline of the development of semantic studies is given. The authors analyze some of the most important stages in the history of the formation of this field. The existing approaches to dealing with form and meaning, namely semasiological and onomasiological ones are discussed. The authors consider the ambiguous character of the notion “semantic change” and analyze the existing alternative definitions that convey the sense of the semantic processes involved. The reasons of semantic change are touched upon. The traditional classification of semantic changes and its characteristics are also considered. The authors use some examples from African varieties of English and French to illustrate some of the semantic processes considered in the article.

Terrific-looking creatures and terrific, funny guys: On the historical development of English terrific

The term terrific, in line with the development of a number of evaluative adjectives over the course of the history of English, such as awesome, bare, brutal, massive and wicked, has come to express positive meanings where it originally conveyed negative ones. This kind of lexical semantic change, well-documented across languages, has been referred to in the literature as ‘(a)melioration’, ‘elevation’ or ‘improvement of meaning’ (cf. Culpeper 1997, among many others). The current paper employs a corpus methodology to trace the history of terrific, using three synchronic and diachronic corpora representing the two supranational varieties of the language, namely British English and American English. The sense development of the adjective is examined in light of parameters such as syntactic function (attributive vs. predicative use), principal collocations, and dialectal variation (British vs. American usage).

Bedeutungswandel und Bezeichnungswandel : von der kognitiven Semasiologie zur kognitiven Onomasiologie

The author integrates the phenomenon of change of meaning, a semasiological problem, into the larger, onomasiological horizon of change of designation, where, apart from change of meaning, word formation, conversion, idioms, etc. on the one hand and lexical borrowing on the other hand are at the centre of interest. A three-dimensional grid for the classification of diachronic-lexical processes is presented. Whereas in a semasiological perspective only very general and open universals of semantic change can be postulated (changes of meaning like metonymy, metaphor, broadening, narrowing, etc.), the onomasiological perspective makes visible much further-reaching and more specific conceptual constants of lexical-semantic change. In this way, we move from a weak to a strong invisible-hand hypothesis for lexical change.

Anything Goes: Extreme Polysemy in Lexical-Semantic Change

Aside from work on grammaticalization and related research, previous approaches to lexical-semantic change typically focus on taxonomies of products of change but do not consider the impetus or process for changes nor how to capture multiple semantic changes. This article undertakes case studies of dope, hot dog, and tweak, taking definitions from the Oxford English Dictionary and Urban Dictionary and then cataloging instances from the Corpus of Historical American English and the Corpus of Contemporary American English to show how variegated change can be within one language in a relatively brief time period, 1812–2012. Usage is tracked over time as well as across text type and in specific fields. Finally, this article concludes with a discussion some implications of the case studies for understanding lexical-semantic change.