Automatic Extraction of Semantic Classes from Syntactic Information in Online Resources
Abstract
This paper addresses the issue of word-sense ambiguity in extraction from machine-readable resources for the construction of large-scale knowledge sources. We describe two experiments: one which took word-sense distinctions into account, resulting in 97.9% accuracy for semantic classification of verbs based on (Levin, 1993); and one which ignored word-sense distinctions, resulting in 6.3% accuracy. These experiments were dual purpose: (1) to validate the central thesis of the of (Levin, 1993), i.e., that verb semantics and syntactic behavior are predictably related; (2) to demonstrate that a 20-fold improvement can be achieved in deriving semantic information from syntactic cues if we first divide the syntactic cues into distinct groupings that correlate with different word senses. Finally, we show that we can provide effective acquisition techniques for novel word senses using a combination of online sources.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 15, 1998
- Accession Number
- AD1006915
Entities
People
- Bonnie J. Dorr
- Doug Jones
Organizations
- University of Maryland