Papers by Ian McKillop

1 papers
LexSubCon: Integrating Knowledge from Lexical Resources into Contextual Embeddings for Lexical Substitution (2022.acl-long)

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Challenge: Lexical substitution is the task of generating meaningful substitutes for a word in a given textual context.
Approach: They propose an end-to-end lexical substitution framework based on contextual embedding models that can identify highly-accurate substitute candidates.
Outcome: The proposed framework outperforms state-of-the-art embedding models on LS07 and CoInCo benchmark datasets by at least 2% over existing embeddable models.

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