C o T o H i L i at LSCD iscovery: the Role of Linguistic Features in Predicting Semantic Change (2022)

Abstract: This paper presents the contributions of the CoToHiLi team for the LSCDiscovery shared task on semantic change in the Spanish language. We participated in both tasks (graded discovery and binary change, including sense gain and sense loss) and proposed models based on word embedding distances combined with hand-crafted linguistic features, including polysemy, number of neological synonyms, and relation to cognates in English. We find that models that include linguistically informed features combined using weights assigned manually by experts lead to promising results.

## introduction in recent years, more and more studies in computational linguistics have focused on the issue of lexical semantic change, tracking the shift in the meaning of words by looking at their usage across time in corpora dating from different time periods @xcite . vector spaces and word embeddings have widely been used for tracking semantic shifts of words across different time periods. previous studies on the computational analysis of lexical semantic change have found that different word properties such as word frequency and polysemy have a role in influencing the potential semantic shift of the word, proposing statistical laws of semantic change such as the law of innovation and the law of differentiation @xcite @xcite . @xcite have proposed that semantic change can be studied cross-lingually, by comparing present meanings of cognate words, which by definition share a common etymon from which the current meanings have diverged. the resulting implication is that analyzing cognates of the target word in other languages can also potentially provide clues regarding the word's prior semantic change. we provide more details on the linguistic motivation for regarding these features as relevant for the task of analyzing semantic change in the following sections. ## background the lscdiscovery shared task (d. @xcite on predicting semantic change for the spanish language consisted of two sub-tasks. for the first task -graded discovery -the participants were asked to rank the set of content words (n, v, a) in the lemma vocabulary intersection of c1 and c2 according to their degree of semantic change between c1 to c2. the predictions were scored against the ground truth via spearman's rank-order correlation coefficient. for the second sub-task -binary change -participants were be asked to classify a pre-selected set of content words (n, v, a) into two classes, 0 for no change and 1 for change. the second sub-task also included two optional sub-tasks on predicting whether the target word undergoing semantic change has gained or lost senses, also formulated as a binary classification problem. submissions were graded using precision, recall and f1-score. the data consisted of two corpora of texts in the spanish language: old corpus, created using different sources freely available from project gutenberg (containing texts published between 1810 -1906), and modern corpus, created using different sources available from the opus project @xcite . we participated in the lscdiscovery shared task on semantic change in the spanish language with submissions in both main sub-tasks: graded discovery and binary change, as well as the optional tasks on sense gain and sense loss. for all tasks we experimented with approaches based on distances in word embedding spaces combined with hand-crafted linguistic features. ## system overview in this section we describe the features and models used to make automatic predictions on the semantic change of target words, for both sub-tasks. we release all the code used for implementing our submissions. 1 the general method for our submissions in all tasks has consisted of computing, for every given target word, several metrics including embedding distances and linguistic hand-crafted features, and subsequently weighing them as features in a model used to predict the final score. the list of features used consists of the following: in the following subsections we describe in detail both the features and the models used to achieve predictions. ## conclusions we have presented our methods and results in participating in the spanish semantic change shared task. we proposed a system based in part on word embedding distances, which are already the norm in sota models for predicting semantic shift @xcite , and in part on handcrafted linguistic features, chosen based on theoretical linguistic motivation and on empirical evidence of their relevance to semantic change. while we have done minimal experimentation with the parameters and settings used in training word embeddings, and used supervised models trained on very little data, we obtain encouraging results. for the future, we suggest that combining embedding models trained with more fine-tuned parameters optimized for the given task along with features such as the ones described could lead to improved results. we conclude that incorporating linguistically informed features (aside from word frequency) in computational models for predicting semantic change is a valuable and currently under-explored avenue.