A Systematic Study of Semantic Vector Space Model Parameters

We present a systematic study of parameters used in the construction of semantic vector space models. Evaluation is carried out on a variety of similarity tasks, including a compositionality dataset, using several source corpora. In addition to recommendations for optimal parameters, we present some novel findings, including a similarity metric that outperforms the alternatives on all tasks considered.

• Publication year

  2014

• Venue

  CVSC@EACL

• Publication date

  2014-04-01

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.3115/v1/W14-1503
• 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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