Paper: The Effects of Lexical Resource Quality on Preference Violation Detection

ACL ID P13-2134
Title The Effects of Lexical Resource Quality on Preference Violation Detection
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2013
Authors

Lexical resources such as WordNet and VerbNet are widely used in a multitude of NLP tasks, as are annotated corpora such as treebanks. Often, the resources are used as-is, without question or exam- ination. This practice risks missing sig- nificant performance gains and even entire techniques. This paper addresses the importance of resource quality through the lens of a challenging NLP task: detecting selec- tional preference violations. We present DAVID, a simple, lexical resource-based preference violation detector. With as- is lexical resources, DAVID achieves an F1-measure of just 28.27%. When the resource entries and parser outputs for a small sample are corrected, however, the F1-measure on that sample jumps from 40% to 61.54%, and performance on other examples rises, suggesting that...