Language Resources and Evaluation

, Volume 41, Issue 1, pp 61–89

Automatically learning semantic knowledge about multiword predicates

Authors

    • Department of Computer ScienceUniversity of Toronto
  • Suzanne Stevenson
    • Department of Computer ScienceUniversity of Toronto
  • Ryan North
    • Department of Computer ScienceUniversity of Toronto
Article

DOI: 10.1007/s10579-007-9017-9

Cite this article as:
Fazly, A., Stevenson, S. & North, R. Lang Resources & Evaluation (2007) 41: 61. doi:10.1007/s10579-007-9017-9

Abstract

Highly frequent and highly polysemous verbs, such as give, take, and make, pose a challenge to automatic lexical acquisition methods. These verbs widely participate in multiword predicates (such as light verb constructions, or LVCs), in which they contribute a broad range of figurative meanings that must be recognized. Here we focus on two properties that are key to the computational treatment of LVCs. First, we consider the degree of figurativeness of the semantic contribution of such a verb to the various LVCs it participates in. Second, we explore the patterns of acceptability of LVCs, and their productivity over semantically related combinations. To assess these properties, we develop statistical measures of figurativeness and acceptability that draw on linguistic properties of LVCs. We demonstrate that these corpus-based measures correlate well with human judgments of the relevant property. We also use the acceptability measure to estimate the degree to which a semantic class of nouns can productively form LVCs with a given verb. The linguistically-motivated measures outperform a standard measure for capturing the strength of collocation of these multiword expressions.

Keywords

Lexical acquisitionCorpus-based statistical measuresVerb semanticsMultiword predicatesLight verb constructions

Copyright information

© Springer Science+Business Media 2007