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A Methodology for Large-Scale, Disambiguated and Unbiased Lexical Knowledge Acquisition Based on Multilingual Word Alignment
2021
Italian Conference on Computational Linguistics
In order to be concretely effective, many NLP applications require the availability of lexical resources providing varied, broadly shared, and language-unbounded lexical information. However, state-ofthe-art knowledge models rarely adopt such a comprehensive and cross-lingual approach to semantics. In this paper, we propose a novel automatable methodology for knowledge modeling based on a multilingual word alignment mechanism that enhances the encoding of unbiased and naturally disambiguated
dblp:conf/clic-it/GrassoC21
fatcat:pyisgjjg5bfcng267wkuqdij3q