Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
An empirical symbolic approach to natural language processing
Artificial Intelligence - Special volume on empirical methods
Forgetting Exceptions is Harmful in Language Learning
Machine Learning - Special issue on natural language learning
An automatic method for generating sense tagged corpora
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Feature Extraction Based on Decision Boundaries
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision tree of bigrams is an accurate predictor of word sense
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Using domain information for word sense disambiguation
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Pattern learning and active feature selection for word sense disambiguation
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
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The objective of this study is to shed more light on the dependence between the performance of WSD feature-based classifiers and the specific features that may be chosen to represent a word context. In this paper we show that the features commonly used to discriminate among different senses of a word (words, keywords, POS tags) are overly sparse to enable the acquisition of truly predictive rules or probabilistic models. Experimental analysis demonstrates (with some surprising result) that the acquired rules are mostly tied to surface phenomena occurring in the learning set data, and do not generalize across hyponimys of a word nor across language domains. This experiment, as conceived, has no practical application in WSD, but clearly shows the positive influence of a more semantically oriented approach to WSD. Our conclusion is that feature-based WSD is at a dead-end, as also confirmed by the recent results of Senseval 2001.