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2021 SemEval SemEval 2021

FII_CROSS at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation

Abstract

AbstractThis paper presents a word-in-context disambiguation system. The task focuses on capturing the polysemous nature of words in a multilingual and cross-lingual setting, without considering a strict inventory of word meanings. The system applies Natural Language Processing algorithms on datasets from SemEval 2021 Task 2, being able to identify the meaning of words for the languages Arabic, Chinese, English, French and Russian, without making use of any additional mono- or multilingual resources.

Authors

Ciprian Bodnar, Andrada Tapuc, Cosmin Pintilie, Daniela Gifu, Diana Trandabat

Topics

Natural Language Processing > Understanding > Semantic Analysis Natural Language Processing > Resources & Methods > Lexical Semantics Natural Language Processing > Resources & Methods > Multilingual NLP

Keywords

natural language processing multilingual nlp semantic representation cross-lingual disambiguation word-in-context disambiguation
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