MARVS Revisited: Incorporating Sense Distribution and Mutual Information into Near-Synonym Analyses

Siaw Fong Chung, Kathleen Virginia Ahrens

Research output: Contribution to journalArticlepeer-review

3 Downloads (Pure)

Abstract

In MARVS (Module-Attribute Representation of Verbal Semantics), verbs are differentiated based on eventive information, which is comprised of event modules and role modules. Huang et al. (2000) used MARVS to examine near-synonyms and suggested that it can highlight the difference between synonymous sets. This paper suggests that the operational steps underlying a MARVS analysis can be improved by analyzing the sense distribution of the near synonyms and by looking at the Mutual Information values of the collocating words. Both these steps increase the verifiability of the semantic analysis in MARVS and set the groundwork for automatic extraction of lexical meaning.
Original languageEnglish
Pages (from-to)415-434
Number of pages20
JournalLanguage and Linguistics
Volume9
Issue number2
Publication statusPublished - 1 Apr 2008

User-Defined Keywords

  • Near-synonyms
  • MARVS
  • sense
  • Mutual Information Value

Fingerprint

Dive into the research topics of 'MARVS Revisited: Incorporating Sense Distribution and Mutual Information into Near-Synonym Analyses'. Together they form a unique fingerprint.

Cite this