Discriminating Homonymy from Polysemy in Wordnets: E nglish, S panish and P olish Nouns (2021)

Abstract: We propose a novel method of homonymy-polysemy discrimination for three Indo-European Languages (English, Spanish and Polish). Support vector machines and LASSO logistic regression were successfully used in this task, outperforming baselines. The feature set utilised lemma properties, gloss similarities, graph distances and polysemy patterns. The proposed ML models performed equally well for English and the other two languages (constituting testing data sets). The algorithms not only ruled out most cases of homonymy but also were efficacious in distinguishing between closer and indirect semantic relatedness.

## introduction lexical polysemy is the word property of being a signifier for different but semantically related senses. homonymy, on the other hand, is the accidental identity of word-forms, with no traces of real semantic relatedness. homonyms have different etymologies, while polysemes are the product of the sense extending diachronic processes (lyons, 1995, pp. 54-60). in fact, homonymous words -as semantically unrelated -should be treated as separate words. for natural language processing this task is completely valid since homonyms frequently appear in textual corpora. wordnets suffer from the absence of explicit links between related meanings and do not distinguish between the two types of ambiguity, making the task harder (freihat et al., 2013b; mihalcea, 2003). in this paper we present a machine learning approach to automatic discrimination of homonymy from polysemy in three languages: english, spanish and polish. we randomly drew samples of noun polysemous lemmas from each wordnet and generated all possible sense couplings. then we cross-checked them in traditional dictionaries in search of their homonymy/polysemy status (section 3.1). each pair was annotated with four different groups of features, representing: lemma properties (sec. 3.2.1), semantic similarities between glosses (3.2.2), graph properties of nodes (3.2.3) and polysemy patterns (3.2.4). having trained ml models on english data, we checked their efficacy on spanish and polish sense pairs (sec. 4.1). then, we passed to the analysis of each model behavior on the subset of english words with known sense distances (we transformed macro-and microstructures of oxford lexico and merriam-webster dictionary into graphs, sec. 4.2). we also introduced a definitional guidelines for distinction between close and indirect polysemy relationship. at the end, we manually inspected 300 sense pairs to assess how well homonymy-polysemy discrimination served close polysemy recognition (sec. 6). we define homonyms or homographs as etymologically unrelated sets of senses, having the same part of speech (pos) category and signified by the same lemma. we abstract from other grammatical properties of nouns, such as the mass/countable noun distinction in english or gender differences in spanish. we say that two nominal senses represent homonymy, if they share the same lemma and whose dictionary equivalents are noted under distinct entries (i.e., in disjoint entry microstructures). ## related work polysemy and homonymy attracted huge researchers' attention. approaches to dissolve the problem could be divided roughly into three main camps: (i) regular polysemy pattern recognition, (ii) polysemy instance recognition and (iii) ontology-based discrimination. our method belongs to the second group. ## conclusions in a small-scale study of 400 nouns from three languages representing different branches of the indo-european family we checked usefulness of two ml models (logistic regression and svm) in discriminating homonymy from polysemy. we proposed a new set of 19 language-independent features, which comprised: lemma properties (like length), gloss similarities (including embeddings), graph properties (like graph distances) and frequent polysemy patterns. lr and svm were trained on english data and tested on spanish and polish. the results were comparable, suggesting that our method could be transferred to noncongenial languages. machine learning models performed above baselines for all languages. comparison with traditional dictionaries showed that trained classifiers preserved not only the polysemy-homonymy distinction, but also favoured direct polysemy over indirect relationships (in the prediction class 'p', with the reversed situation for 'h' predictions). manual inspection of the lr 'p'-class outcome confirmed this finding: majority of sense pairs were classified as close rather than indirect semantic links.