Supervised Phrase-boundary Embeddings

We propose a new word embedding model, called SPhrase, that incorporates supervised phrase information. Our method modifies traditional word embeddings by ensuring that all target words in a phrase have exactly the same context. We demonstrate that including this information within a context window produces superior embeddings for both intrinsic evaluation tasks and downstream extrinsic tasks.

• Publication year

  2020

• Venue

  International Symposium on Intelligent Data Analysis

• Publication date

  2020-02-15

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1007/978-3-030-44584-3_37arXiv 2002.06450
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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