Abstract

This paper describes a state-of-the-art supervised, knowledge-intensive approach to the automatic identification of semantic relations between nominals in English sentences. The system employs a combination of rich and varied sets of new and previously used lexical, syntactic, and semantic features extracted from various knowledge sources such as WordNet and additional annotated corpora. The system ranked first at the third most popular SemEval 2007 Task - Classification of Semantic Relations between Nominals and achieved an F-measure of 72.4% and an accuracy of 76.3%. We also show that some semantic relations are better suited for WordNet-based models than other relations. Additionally, we make a distinction between out-of-context (regular) examples and those that require sentence context for relation identification and show that contextual data are important for the performance of a noun-noun semantic parser. Finally, learning curves show that the task difficulty varies across relations and that our learned WordNet-based representation is highly accurate so the performance results suggest the upper bound on what this representation can do.

Original languageEnglish (US)
Pages (from-to)589-610
Number of pages22
JournalInformation Processing and Management
Volume46
Issue number5
DOIs
StatePublished - Sep 1 2010

Keywords

  • Lexical semantics
  • Machine learning
  • Natural language processing
  • Semantic relations

ASJC Scopus subject areas

  • Information Systems
  • Media Technology
  • Computer Science Applications
  • Management Science and Operations Research
  • Library and Information Sciences

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