Data-driven Synset Induction and Disambiguation for Wordnet Development

International audienceAutomatic methods for wordnet development in languages other than English generally exploit information found in Princeton WordNet (PWN) and translations extracted from parallel corpora. A common approach consists in preserving the structure of PWN and transferring its content in new languages using alignments, possibly combined with information extracted from multilingual semantic resources. Even if the role of PWN remains central in this process, these automatic methods offer an alternative to the manual elaboration of new wordnets. However, their limited coverage has a strong impact on that of the resulting resources. Following this line of research, we apply a cross-lingual word sense disambiguation method to wordnet development. Our approach exploits the output of a data-driven sense induction method that generates sense clusters in new languages, similar to wordnet synsets, by identifying word senses and relations in parallel corpora. We apply our cross-lingual word sense disambiguation method to the task of enriching a French wordnet resource, the WOLF, and show how it can be efficiently used for increasing its coverage. Although our experiments involve the English-French language pair, the proposed methodology is general enough to be applied to the development of wordnet resources in other languages for which parallel corpora are available. Finally, we show how the disambiguation output can serve to reduce the granularity of new wordnets and the degree of polysemy present in PWN

Translation-oriented Word Sense Induction Based on Parallel Corpora

Acquisition de sens lexicaux, désambiguïsation lexicale, clustering, traduction, sélection lexicale, corpus parallèlesInternational audienceWord Sense Disambiguation (WSD) is an intermediate task that serves as a means to an end defined by the application in which it is to be used. However, different applications have varying disambiguation needs which should have an impact on the choice of the method and of the sense inventory used. The tendency towards application-oriented WSD becomes more and more evident, mostly because of the inadequacy of predefined sense inventories and the inefficacy of application-independent methods in accomplishing specific tasks. In this article, we present a data-driven method of sense induction, which combines contextual and translation information coming from a bilingual parallel training corpus. It consists of an unsupervised method that clusters semantically similar translation equivalents of source language (SL) polysemous words. The created clusters are projected on the SL words revealing their sense distinctions. Clustered equivalents describing a sense of a polysemous word can be considered as more or less commutable translations for an instance of the word carrying this sense. The resulting sense clusters can thus be used for WSD and sense annotation, as well as for lexical selection in translation applications