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    • Villavicencio, A. (2)
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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 2 of 2 1 Understanding the effects of negative (and positive) pointwise mutual information on word vectors Salle, A.; Villavicencio, A.. - : Taylor & Francis, 2022 Abstract: Despite the recent popularity of contextual word embeddings, static word embeddings still dominate lexical semantic tasks, making their study of continued relevance. A widely adopted family of such static word embeddings is derived by explicitly factorizing the Pointwise Mutual Information (PMI) weighting of the cooccurrence matrix. As unobserved cooccurrences lead PMI to negative infinity, a common workaround is to clip negative PMI at 0. However, it is unclear what information is lost by collapsing negative PMI values to 0. To answer this question, we isolate and study the effects of negative (and positive) PMI on the semantics and geometry of models adopting factorization of different PMI matrices. Word and sentence-level evaluations show that only accounting for positive PMI in the factorization strongly captures both semantics and syntax, whereas using only negative PMI captures little of semantics but a surprising amount of syntactic information. Results also reveal that incorporating negative PMI induces stronger rank invariance of vector norms and direction, as well as improved rare word representations. URL: https://eprints.whiterose.ac.uk/184560/ BASE Hide details 2 Improving tokenisation by alternative treatment of spaces Gow-Smith, E.; Scarton, C.; Madabushi, H.T.. - 2022 BASE Show details

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