Translation-based Lexicalization Generation and Lexical Gap Detection: Application to Kinship Terms (2024)

Abstract: Constructing lexicons with explicitly identified lexical gaps is a vital part of building multilingual lexical resources. Prior work has leveraged bilingual dictionaries and linguistic typologies for semi-automatic identification of lexical gaps. Instead, we propose a generally-applicable algorithmic method to automatically generate concept lexicalizations, which is based on machine translation and hypernymy relations between concepts. The absence of a lexicalization implies a lexical gap. We apply our method to kinship terms, which make a suitable case study because of their explicit definitions and regular structure. Empirical evaluations demonstrate that our approach yields higher accuracy than BabelNet and ChatGPT. Our error analysis indicates that enhancing the quality of translations can further improve the accuracy of our method.

## introduction in computational semantics, the term lexicon refers to the vocabulary which speakers of the language use to express concepts. a language l lexicalizes a concept s if it has a lexeme (or a word) that can express s; otherwise s corresponds to a lexical gap in l (murphy and @xcite . for example, the polish word pojutrze is a lexicalization of the concept "the day after tomorrow" which corresponds to a lexical gap in english. in particular, kinship terms describe familial relations such as "grandparent" and "female cousin". the clear definitions (glosses) and regular hierarchical structure of kinship concepts make them well-suited for investigations into lexicons and lexical gaps. the task of identifying lexicalizations for a given concept underlies the automatic construction of multilingual wordnets @xcite , lexical knowledge bases modeled after the princeton wordnet @xcite . wordnets are comprised of synonym sets (synsets), each of which zh: 我有一个 堂哥 ，但是我没 堂姐 。 en: i have a cousin , but i have no cousin . prior work on semi-automatic identification of concept lexicalizations and lexical gaps has leveraged bilingual dictionaries, wordnets and linguistic typologies. @xcite apply a decision tree approach based on information from a machine-readable bilingual dictionary, but their experiments are restricted to english and italian. @xcite focus on a subset of bilingual action verbs in the context of video-based translation, but establish no mapping to wordnettype concepts. @xcite compile a dataset of 1911 kinship terms and a list of lexical gaps in 176 languages by combining native speaker expertise in 10 languages, lexicalization information from wiktionary, and a set of typological patterns of @xcite . we leverage their resource to develop a method which is fully automated, language-independent, and not specific to kinship terms. the principal idea behind our approach is to reduce the task of lexical gap detection to the task of lexicalization generation. the latter can be viewed as populating a concept-language matrix (figure 1 ) in which each cell contains a lexicalization of the concept in the corresponding language, if one exists. given a correctly populated lexicalization matrix, empty cells correspond to lexical gaps. the task of lexicalization generation differs from the task of synset population in that the former requires at most one rather than all concept lexicalizations. we propose a translation-based method for lexicalization generation, which we also apply to lexical gap detection. our method is grounded in theoretical propositions based on the hypernym/hyponym relationships between concepts, which provide a basis for detecting incorrect lexicalizations. in particular, our model predicts that languages tend to avoid ambiguity between lexicalizations within disjunctive triples of concepts such as parent/mother/father. we develop a method for lexical gap detection via filtering concept lexicalizations produced by translating unambiguous "seed" words in the context of the corresponding concept glosses. we leverage existing lexical knowledge bases and machine translation systems, as well as the tree structure of kinship concepts, to decide if a given translation is literal or indicative of a lexical gap. while we focus on kinship terms in this work, our approach is also applicable to other domains. our evaluation on kinship terms across 10 languages demonstrates substantial improvements over babelnet and chatgpt. babelnet fails to represent many of the concepts that are not lexicalized in english, while chatgpt exhibits a tendency to generate overly specific or irrelevant responses. we identify three main causes of errors made by our algorithm: inaccurate translations, non-standard terms, as well as errors and omissions in the benchmark dataset itself. we release our code and babel-net concept mapping on github. ## theoretical framework we start this section by discussing the linguistic background related to the issue of lexical gaps. we then formally define the tasks addressed in this paper, as well as related theoretical concepts, such as literal translations, seed words, and disjunctive concept triples. this is followed by propositions and proofs that form the theoretical basis of our method. ## methodology in this section, we describe our approach to lexicalization generation. the essence of the method is to generate a candidate lexicalization for each concept by translating a seed word into the target language in the context of the concept gloss, and then apply a series of filters to remove incorrect candidates. for each concept, we output the corresponding lexicalization if it has not been filtered out, or gap otherwise. ## experiments this section is devoted to the empirical evaluation of our method, and includes information on our datasets, resources, metrics, and baselines. our principal dataset is the kinship database described in section 3. we develop our method on 3 languages: english (eng), chinese (zho), and persian (pes). we test it on 10 languages: spanish (spa), russian (rus), french (fra), german (deu), polish (pol), arabic (ara), italian (ita), mongolian (mon), hungarian (hun), and hindi (hin). for each language, we test on the set of 71 concepts which are relatively well represented in the kinship database. ## conclusion we proposed a novel computational method that generates concept lexicalizations and detects lexical gaps. the method is grounded in formal definitions and propositions, and leverages translation and hypernym/hyponym taxonomy relations. we also presented an algorithm that generates both kinship concepts and the relations between them. experimental results on diverse languages confirm the effectiveness of our approach. our approach is general and applicable to other domains.