Automatic Extraction of Semantic Classes from Syntactic
Information in Online Resources
Automatic Extraction of Semantic Classes from Syntactic
Information in Online Resources
Files
Publication or External Link
Date
1998-10-15
Authors
Dorr, Bonnie J.
Jones, Doug
Advisor
Citation
DRUM DOI
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 work 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.
(Also cross-referenced as UMIACS-TR-95-65)