Deconstructing destruction: A Cognitive Linguistics perspective on a computational analysis of diachronic change (2022)

Abstract: In this paper, we aim to introduce a Cognitive Linguistics perspective into a computational analysis of near-synonyms. We focus on a single set of Dutch near-synonyms, vernielen and vernietigen, roughly translated as ‘to destroy’, replicating the analysis from Geeraerts (1997) with distributional models. Our analysis, which tracks the meaning of both words in a corpus of 16th-20th century prose data, shows that both lexical items have undergone semantic change, led by differences in their prototypical semantic core.

## introduction this paper aims to stimulate further convergence between cognitive linguistics approaches to language change and computational methods for semantic change. cognitive linguistics is a contemporary linguistic paradigm that assumes that linguistic knowledge is rooted in general cognitive capabilities, that language is shaped by usage and that meaning entails conceptualization @xcite . it pays particular attention to the interaction between semasiological change, whereby a word's meaning changes over time, and onomasiological change, whereby the semantic configuration of a set of (near-)synonyms is reorganized over time. in this paper we focus on a single set of dutch near-synonyms, vernielen and vernietigen, roughly translated as 'to destroy'. we replicate the analysis of these verbs from @xcite with distributional models. the analysis, which tracks the meaning of both words in a corpus of 16th-20th century prose data, shows that both lexical items have undergone semantic change led by differences in their prototypical semantic core, as predicted by geeraerts' work. ## semasiology and onomasiology in cognitive linguistics following from "the cognitive commitment", which entails that the description of human language should be congruent with what is known about cognition within and outside of linguistics @xcite , cognitive linguistics aims to be a psychologically plausible model. language is, thus, primarily studied as a means to communicate -a way to convey and process meaning. furthermore, a maximalist, non-reductionist perspective on linguistic knowledge is assumed. language systems are considered to be "reflections of general conceptual organization, categorization principles, processing mechanisms, and experiential and environmental influences" @xcite . the movement, therefore, places a large emphasis on meaning. @xcite examine the structure of lexical variation in the use of clothing terminology in dutch. crucially, it is the first study to systematically emphasize the importance of two distinctions. on the one hand, it shows that in order to obtain a full picture of the structure of lexical variation, semasiological research should be complemented with an onomasiological approach. the semasiological perspective examines the range of applications of a particular expression. semasiology is, for this reason, often defined as research into the meaning of a particular item: given a particular word or expression, what are the referents to which the word applies? in the case of the dutch word monitor, for instance, a semasiological analysis would reveal that it can refer both to a supervi-sor, and to a computer screen (see @xcite . the onomasiological perspective investigates naming rather than meaning. an onomasiological approach, thus, starts from a particular (type of) referent or concept and determines which names exist or can be used to refer to the referent. for instance, an onomasiological analysis of the concept computer screen in dutch would reveal that both monitor and computerscherm can be used to express this concept. on the other hand, @xcite was the first study to make the importance of the interaction between four different types of lexical variation for the structure of the lexicon explicit. first, it examines semasiological variation, the situation where a single lexical item can refer to more than one referent. for example, the lexical item pants can both be used to refer to a two-legged type of outer garment (in general), but also to a more specific referent, viz. men's un-derwear. the second and third types of lexical variation that are distinguished concern two varieties of onomasiological variation: conceptual onomasiological variation and formal onomasiological variation. conceptual onomasiological variation concerns the situation where "a referent or type of referent may be named by means of various conceptually distinct lexical categories" @xcite . for example, to refer to a pair of blue jeans, a language user can either choose to select a lexical item belonging to the concept blue jeans and use a word like jeans or blue jeans, or (s)he can conceptualize the referent as a type of pants, a superordinate concept, and call the denotatum trousers or pants. formal onomasiological variation occurs when a choice has to be made between different synonymous expressions for a referent. in the blue jeans example, this would involve determining the relative frequency of the terms jeans versus blue jeans versus trousers versus pants. finally, it shows how contextual variation can be at play both at the semasiological and onomasiological level. contextual variation (also called speaker and situation related variation) is broadly defined: it includes both the relatively stable lectal properties of the interlocutors involved (like their gender or their nationality), but also transient situation-related features, like the register of the speech event @xcite . for the (onomasiological) blue jeans example, for instance, contextual variation may take the form of determining whether older people are more likely to refer to the concept as blue jeans. the program laid out in @xcite was applied to diachronic change in geeraerts @xcite . an important finding of this work is that semasiological and onomasiological variation and change are not independent of each other: semasiological changes also affect the onomasiological structure of a language. ## destructive verbs in dutch a classical analysis of the verbs meaning 'to destroy' in dutch is presented in @xcite @xcite . in these papers, geeraerts analyzes vernielen and vernietigen in 19th century data taken from the citations corpus for largest historical dictionary of dutch, the woordenboek der nederlandsche taal 'dictionary of the dutch language'. etymologically, the near-synonyms do not have the same root. vernielen is a verb formed with a verbalizing prefix ver-, and niel, an obsolete dutch adjective that roughly translates to 'down to the ground'. the literal meaning of the verb vernielen is then 'to throw down to the ground, to tear down'. vernietigen, in contrast, is based on a verbalizing prefix ver-, with the adjective nietig (which itself comes from niet 'not, nothing' + a suffix -ig). the meaning of vernietigen is then 'to annihilate, to bring to naught'. despite these divergent sources, geeraerts shows that the near-synonyms can be used in similar contexts by the same author in the 19th century. for instance, in examples 1 and 2 (adapted from @xcite , vernielen and vernietigen occur in the context of a material artefact (a part of a building) being destroyed. he discusses many more examples that clearly show that the verbs are interchangeable in 19th century dutch. overall, three semantic groups of uses for vernielen and vernietigen can be distinguished in his data ( ## data and methods in the analysis, we use a corpus of prose texts from dbnl, the digitale bibliotheek voor de nederlandse letteren 'digital library for dutch languages and literature'. some information about the corpus can be found in @xcite , though the corpus is not publicly available at this time. we specifically extracted all corpus texts tagged as prose in the metadata from the 16th, 17th, 18th, 19th and 20th century. due to data sparseness, we combine the subcorpora for the 16th and 17th century in the analysis. as no high-quality lemmatizers or pos-taggers are as of yet available for historical dutch, the only preprocessing we applied to the corpus was to transform the entire corpus to lower case and to automatically indicate sentence boundaries using the pretrained nltk sentence tokenizer @xcite . next, we extracted all tokens for vernielen and vernietigen (including inflected forms and spelling variants) from the four subcorpora and took a random sample of @xmath0. then, we constructed a single vector space model for the tokens in each subcorpus. the models that we built are based on the procedure outlined in @xcite . more specifically, we construct a vector representation for each token in the corpus, using the words in the context of the tokens to construct the vectors (first order vectors). we supplement this information by also constructing a vector for each of the relevant context words (second order vectors). an association strength measure is used rather than raw co-occurrence frequencies, namely positive pointwise mutual information or ppmi @xcite @xcite . this procedure has the advantage that both the context words for a token, as well as their semantic similarity with other context words, is taken into account (see @xcite . this method represents an example of a context-counting distributional model, which we opt for here (rather than for a context-predicting method) because we want to keep the results as comparable as possible to the results obtained in @xcite on contemporary data. one line of future research is to replicate the results obtained here with diachronic contextualized word embeddings when they become available for dutch. the parameters that we used are largely based on the best model found in @xcite for the analysis of vernielen and vernietigen in 21st century newspaper data. however, since the diachronic corpus that we use is not lemmatized or pos-tagged, the vectors represent word forms rather than lemmas and we do not use part-of-speech filters. additionally, we decided to decrease the window size from 15 to 10 words to the left and right of the target token for the first-order context words because preliminary analyses revealed that in models with a broader window, too many irrelevant or noisy context features were included in the analysis (also due to the fact that pos filters cannot be applied). the parameters settings that we used are the following: the models were constructed with the nephosem python library @xcite . the result of this procedure is a token-by-context matrix with 5000 dimensions, where the dimensions represent second order context vectors, i.e. the vectors of the context words around each token. since for some tokens no relevant context words are found with our parameter settings, these tokens are excluded from the remainder of the analysis. for clustering and visualization, we transformed this matrix into a square distance matrix by computing the cosine vector of each pair of token-level vectors, without further dimensionality reduction beforehand. thus, this final matrix describes the dissimilarity between the vector of each token and all other token vectors in the subcorpus. as a next step, we submitted each model to a clustering procedure in r (r core team, 2020). we used hierarchical clustering (ward method), distinguishing four clusters, following the procedure in @xcite for maximal comparability. finally, we analyzed each cluster per subcorpus basing ourselves on a procedure outlined in @xcite that is available in the python library sema-sioflow @xcite . the procedure consists of a number of steps. first, the relevant context words, on which the token vectors are based, are extracted from the model data. then, after each token is assigned to a cluster, we calculate how often a specific context word occurs within a particular cluster and outside of the cluster. using this information, we can calculate which context words have an exceptionally high frequency in each cluster and therefore represent the semantics of each cluster well. in the analysis, we will only consider context words for which at least 50% of their occurrences are within the cluster of interest. tables 2-5 show an overview of the most important context words per period and per cluster, obtained with the procedure outlined above. only context words with a frequency of more than 2 are shown, to avoid that infrequent words get too much weight in the interpretation. the first column also shows an interpretation of each cluster. the final columns indicate the relative and absolute proportion of each variant in the cluster. in the first subcorpus (16th-17th century, in the 18th century subcorpus ( in the 19th century ( finally, in the subcorpus for the 20th century ( first, vernietigen is the most frequent variant in cluster 1, which is a diverse cluster, with the most frequent context word related to complete destruction (geheel 'completely'), but also consisting of other types of lexical items such as abstract concepts. in cluster 2, vernietigen is the most frequent variant as well. this is a semantic cluster with many war-related lexical items, although it also contains other concrete objects. the context words in cluster 3, where vernietigen is also the most frequent variant, are mostly function words, such as adverbs and reflexive pronouns. this cluster is not determined by semantic similarity between the tokens, but rather by the type of construction the tokens occur in. the context words in cluster 4, where both vernielen and vernietigen are possible, are mostly related to (parts of) buildings. this is a clear change compared to the earlier data, where the destruction of parts of building correlated strongly with the use of vernielen. however, the context words in this cluster have quite a low frequency so likely not all tokens are related to the destruction of (parts of) buildings: perhaps vernielen has become so infrequent that even this prototypical use is not frequent enough anymore to be distinguished by the model and clustering procedure. ## discussion the models for the four subcorpora show how the relationship between the near-synonyms vernielen and vernietigen has changed over time. semasiologically, vernielen was the major variant in the 16th and 17th century, occurring in tokens related to the death of persons and concrete, natural objects. over the course of the 18th century, it developed its prototypical meaning related to the destruction of (parts of) buildings, often by fire, and this meaning remained its core usage in the 19th century. by the 20th century, the verb had decreased in frequency and its prototypical core was no longer distinguishable from the data. vernietigen, in contrast, was the less frequent variant in the 16th and 17th century and at that time, there were no clear contexts yet where the verb occurred. it was mostly found in a semantically diverse cluster where its nearsynonym vernielen was possible as well. from the 18th century onwards, the verb started to increase in frequency and it developed its prototypical sense of being used with abstract objects. in the 19th century, it also started to invade contexts where vernielen was preferred before (specifically related to the death of persons and to war). in the 20th century data, we also found a syntactic cluster, consisting of function words that often occur in the context of vernietigen. onomasiologically, the analysis showcased how the nuances in the concept 'to destroy' evolve over time and have become more outspoken. for instance, the clusters related to the destruction of parts of buildings are not yet visible in the oldest data but they are important clusters in the more recent datasets. similarly, the cluster with abstract objects is not yet distinguished by the analysis for the 16th and 17th century, but these objects form a cluster on their own in the 18th and 19th century data. moreover, the analysis also showed how these particular nuances of meaning are typically expressed by a particular verb. in the visualization, for instance, there is clearly less overlap (or interchangeability) between the verbs in the later periods (except in the 20th century data, where vernielen is infrequent). thus, this case-study showcases an example of how formal onomasiological variation and conceptual onomasiological variation can interact. on the one hand, vernielen and vernietigen serve as formal alternatives in the largest cluster from the 16th and 17th century data. however, from the 18th century onwards, each verb increasingly retreats to its prototypical core. arguably, they should therefore be considered conceptually distinct, prototypically referring to different nuances of meaning, even though they remain nearly synonymous. methodologically, our usage of distributional models combined with a cluster analysis and the method, developed in @xcite , to analyze the context words that are good representatives for the clusters, allowed us to show how both verbs changed semantically over time. the procedure employed was quite straightforward, using a single set of parameter settings to model tokens from four diachronic subcorpora. with this procedure, we extended the analyses in @xcite and @xcite to a much longer time span. despite the fact that we used a completely different dataset (a continuous diachronic corpus rather than dictionary citations from the 19th century only) and analysis method (an automatic procedure rather than a manual linguistic analysis), the hypotheses outlined in geeraerts (1997) were mostly confirmed. further, this method allowed us to track semasiological change and to investigate how this interacts with onomasiological variation over time. one shortcoming of the approach is that the ideal settings for the parameters need not be the same for other near-synonyms or for a comparable linguistic alternation in other languages. in fact, this is one of the major findings of @xcite , who showed that there is no direct link between a choice of parameters and the linguistic phenomena that are revealed by a model constructed with the method proposed by @xcite . therefore, while in this contribution we focus on a single set of parameters settings that were shown to be useful in analyses of the same linguistic example in another century, an alternative approach, that has been successfully employed in @xcite , is to consider a broader number of parameter settings to analyze linguistic phenomena.