_language-modelling-makes-sense-propagating-representations-throughwordnet-for-full-coverage-word-sense-disambiguation.jpg "Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation from nn models Video Screenshot Preview")
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⏲ Duration: 9 min 34 sec
✓ Published: 17-Jan-2020
✓ Published: 17-Jan-2020
Description:
Authors: Daniel Loureiro, Alípio JorgennContextual embeddings represent a new generation of semantic representations learned from Neural Language Modelling (NLM) that addresses the issue of meaning conflation hampering traditional word embeddings. In this work, we show that contextual embeddings can be used to achieve unprecedented gains in Word Sense Disambiguation (WSD) tasks. Our approach focuses on creating sense-level embeddings with full-coverage of WordNet, and without recourse to expli
Authors: Daniel Loureiro, Alípio JorgennContextual embeddings represent a new generation of semantic representations learned from Neural Language Modelling (NLM) that addresses the issue of meaning conflation hampering traditional word embeddings. In this work, we show that contextual embeddings can be used to achieve unprecedented gains in Word Sense Disambiguation (WSD) tasks. Our approach focuses on creating sense-level embeddings with full-coverage of WordNet, and without recourse to expli
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