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VERSION:2.0
PRODID:-//University of Liverpool Computer Science Seminar System//v2//EN
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DTSTAMP:20260910T232452Z
UID:Seminar-DMML-1110@lxserverM.csc.liv.ac.uk
ORGANIZER:CN=Danushka Bollegala:MAILTO:Danushka.Bollegala@liverpool.ac.uk
DTSTART:20210303T110000
DTEND:20210303T120000
SUMMARY:Data Mining and Machine Learning Series
DESCRIPTION:Yi Zhou: Learning Sense-Specific Static Word Embeddings using Contextualised Word Embeddings as a Proxy\n\nContextualised word embeddings such as BERT represent a word with a vector that considers the semantics of the target word as well its context. On the other hand, static word embeddings such as GloVe represent words by relatively low-dimensional, memory- and compute-efficient vectors but are not sensitive to the different senses of the word. In this project, we propose a method that extracts sense related information from pretrained contextualised embeddings and inject into static embeddings to create sense-specific static embeddings. Experimental results on multiple benchmarks for word sense disambiguation and sense discrimination tasks show that the proposed method can accurately learn sense-specific static embeddings, outperforming previously proposed sense-specific word embedding learning methods.\n\nhttps://www.csc.liv.ac.uk/research/seminars/abstract.php?id=1110
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