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.
|Number of pages||20|
|Journal||Language and Linguistics|
|Publication status||Published - 1 Apr 2008|
- Mutual Information Value