Identifying semantic relations in context: Near-misses and overlaps

Alla Rozovskaya, Roxana Girju

Research output: Contribution to conferencePaper

Abstract

This paper addresses the problem of semantic relation identification for a set of relations difficult to differentiate: near-misses and overlaps. Based on empirical observations on a fairly large dataset of such examples we provide an analysis and a taxonomy of such cases. Using this taxonomy we create various contingency sets of relations. These semantic categories are automatically identified by training and testing three state-of-the-art semantic classifiers employing various feature sets. The results show that in order to identify such near-misses and overlaps accurately, a semantic relation identification system needs to go beyond the ontological information of the two nouns and rely heavily on contextual and pragmatic knowledge.

Original languageEnglish (US)
StatePublished - Sep 2009

Keywords

  • Lexical semantics
  • Machine learning
  • Semantic relations

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software
  • Electrical and Electronic Engineering

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