Homonymy and Polysemy Detection with Multilingual Information (2021)

Abstract: Deciding whether a semantically ambiguous word is homonymous or polysemous is equivalent to establishing whether it has any pair of senses that are semantically unrelated. We present novel methods for this task that leverage information from multilingual lexical resources. We formally prove the theoretical properties that provide the foundation for our methods. In particular, we show how the One Homonym Per Translation hypothesis of Hauer and Kondrak (2020a) follows from the synset properties formulated by Hauer and Kondrak (2020b). Experimental evaluation shows that our approach sets a new state of the art for homonymy detection.

## introduction a word with multiple senses considered to be semantically ambiguous. in wordnet @xcite , a word is semantically ambiguous if and only if it occurs in more than one synset. there are two types of word ambiguity: two senses of a word are in the relation of polysemy if they are semantically related. for example, the wordnet senses bank#n#2 "financial institution" and bank#n#9 "financial building" are semantically related. two senses of a word which are not semantically related are in the relation of homonymy. continuing the example, bank#n#1 "sloping land" is not related to bank#n#2 "financial institution." a word is homonymous if and only if it has a pair of senses in the homonymy relation. a word which is ambiguous, but not homonymous, is polysemous. polysemy classification @xcite , or homonym detection, is the task of automatically deciding whether a given ambiguous word is homonymous or polysemous. in this paper, we develop and present novel methods for this task. homonymy detection is vital to the tasks of defining sense inventories and clustering finegrained senses @xcite . distinguishing between homonymous and polysemous words is a core problem in lexicography @xcite . it has also been a subject of study in psycholinguistics @xcite . consistent with the well-known tendency for distinct senses of a word to translate differently in other languages @xcite , @xcite show that special processing of homonymous words can improve neural machine translation. deciding whether a given word is homonymous or polysemous typically requires extensive manual effort and consultation of hand-crafted resources, as the intuitions of native speakers alone are not sufficient. wordnet does not contain homonymy information. @xcite rely upon a list of homonymous words obtained from a wikipedia article, which is not reliable. @xcite manually identify a set of homonyms using a dictionary. noting the lack of of any existing homonymy resources of sufficient quality and coverage, @xcite manually construct a list of homonyms from english etymological dictionaries, and map these homonyms to wordnet senses. while the list is not exhaustive, it provides a benchmark to evaluate homonymy detection methods. we adopt a graph-based approach, constructing a sense graph for an input word using wordnet, and a multilingual extension, babelnet @xcite . in such a graph, vertices are senses, while edges represent semantic relatedness. we make the simplifying assumption that the polysemy relation is transitive. thus, any pair of senses which are connected in the sense graph are semantically related. furthermore, if the graph has more than one connected component, the word is homonymous, as it has at least one pair of unrelated senses. thus, the task of identifying homonymous words is reduced to task of deciding the pairwise semantic relatedness of senses. we present a variety of methods for this sub-task, which leverage both monolingual and multilingual information. we formally prove the theoretical properties that provide the foundation for our methods. in particular, we show how the one homonym per translation (ohpt) hypothesis of @xcite follows from the synset properties formulated by @xcite . the results of our experiments set a new state of the art for the task of homonym detection, outperforming the prior work of van den beukel and aroyo (2018). on a balanced dataset of homonymous and polysemous words, we achieve a 12% improvement in f 1 score. we also investigate which combination of translation languages yields the best overall results. ## related work homonymy detection has been investigated in natural language processing. @xcite propose a statistical model which computes a polysemy index on the scale between homonymy and polysemy. their work is not comparable to ours, as it depends upon an additional sources of ontological information. more recently, van den beukel and aroyo (2018) detect homonymous words for the task of humor recognition. their method uses wordnet path similarity @xcite and the textual similarity between synset definitions. their definition of homonyms is broader than ours, including distinct word forms with identical pronunciations (homophones). homonymy detection has also been studied in psycholinguistics. @xcite distinguish between monosemous, polysemous, and homonymous words using word embeddings and contextual embeddings. the idea is that the embedding of a monosemous word should be closer to the embedding of its context than a polysemous word, which should in turn exhibit greater similarity to its context compared to a homonymous word. @xcite extract a list of 534 homonymous words from the wordsmyth dictionary, and annotate them manually in sentential contexts. these resources are then used to analyze the relative frequencies of these homonyms. as homonymous words are exactly those with semantically unrelated senses, the study of homonymy is closely related to the study of se-mantic relatedness between word senses. to this end, @xcite presents methods aimed at automatic construction of a wordnet-like resource using information extracted from parallel text corpora. this involves the use of translation information to induce semantic fields, which partition senses according to their semantic relatedness. @xcite identify semantic relations between words using distributional information extracted from corpora, and show that leveraging multilingual data yields substantial improvements for the task of detecting synonymous words. ## background our work builds upon recent investigations into the linguistic phenomena of sense, homonymy, polysemy, synonymy, and translation. therefore, we begin with a review of the relevant terminology, definitions, and general background knowledge. ## methods in this section, we present our graph-based method for deciding whether a given word is homonymous or not. under our definitions, this is equivalent to deciding whether the word has any senses that are semantically unrelated. operating under the assumption that semantic relatedness of senses is symmetric and transitive, our strategy is as follows: one strength of this method is its lack of dependence on any hyperparameters or additional training data. a vector-based method leveraging distributional semantics, for example, would necessarily depend on some continuous measure of semantic similarity. this in turn would require access to a large text corpus to learn distributional embeddings. in addition, a threshold value would need to be tuned, which would depend on the embeddings and the corpus. by avoiding such requirements, our method is easier to apply to arbitrary domains, and can be applied to any language which is represented in a multi-wordnet. ## experiments having provided theoretical support for our homonym detection methods in section 4, we now empirically evaluate their ability to distinguish between homonymous and polysemous words. we start by describing our datasets and resources, as well as the method for obtaining translations of senses. we then present our results on both words and senses. ## conclusion we have presented a novel approach to the problem of distinguishing between homonymy and polysemy. our methods for establishing semantic relatedness leverage sense translation information from a multi-wordnet, and are supported by proofs constructed upon a formal theory of senses, synonymy, and translation. our approach sets a new state of the art for the task of homonym detection. in the future, we would like to investigate stochas-tic methods for this task, including random walks on semantic graphs, as well as the use of graph embeddings to compute the similarity of concepts in a dense vector space. to facilitate further research on homonymy detection, we make the augmented homonymy resource publicly available. 5