Boosting the Coverage of a Semantic Lexicon by Automatically Extracted Event Nominalizations

Abstract : An important trend in recent works on lexical semantics has been the development of learning methods capable of extracting semantic information from text corpora. The majority of these methods are based on the distributional hypothesis of meaning and acquire semantic information by identifying distributional patterns in texts. In this article, we present a distributional analysis method for extracting nominalization relations from monolingual corpora. The acquisition method makes use of distributional and morphological information to select nominalization candidates. We explain how the learning is performed on a dependency annotated corpus and describe the nominalization results. Furthermore, we show how these results served to enrich an existing lexical resource, the WOLF (Wordnet Libre du Français). We present the techniques that we developed in order to integrate the new information into WOLF, based on both its structure and content. Finally, we evaluate the validity of the automatically obtained information and the correctness of its integration into the semantic resource. The method proved to be useful for boosting the coverage of WOLF and presents the advantage of filling verbal synsets, which are particularly difficult to handle due to the high level of verbal polysemy.
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Communication dans un congrès
LREC 2012 - Eighth International Conference on Language Resources and Evaluation, May 2012, Istanbul, Turkey. 2012
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Kata Gábor, Marianna Apidianaki, Benoît Sagot, Éric Villemonte de La Clergerie. Boosting the Coverage of a Semantic Lexicon by Automatically Extracted Event Nominalizations. LREC 2012 - Eighth International Conference on Language Resources and Evaluation, May 2012, Istanbul, Turkey. 2012. 〈hal-00703127〉

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