Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Elements of information theory
Elements of information theory
Similarity-Based Models of Word Cooccurrence Probabilities
Machine Learning - Special issue on natural language learning
Foundations of statistical natural language processing
Foundations of statistical natural language processing
The disambiguation of nominalizations
Computational Linguistics
Using semantic preferences to identify verbal participation in role switching alternations
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Automatic extraction of subcategorization from corpora
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Measures of distributional similarity
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Semantically motivated subcategorization acquisition
ULA '02 Proceedings of the ACL-02 workshop on Unsupervised lexical acquisition - Volume 9
Improving subcategorization acquisition using word sense disambiguation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Extended lexical-semantic classification of English verbs
CLS '04 Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics
Refining the most frequent sense baseline
DEW '09 Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions
CACLA '07 Proceedings of the Workshop on Cognitive Aspects of Computational Language Acquisition
Classifying Japanese polysemous verbs based on fuzzy C-means clustering
TextGraphs-4 Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing
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Some statistical learning systems are evaluated using measures of distributional similarity. To deal with the problem of zero events in the distributions under comparison, smoothing is frequently performed before similarity measures are applied. Smoothing alters the information in the original distribution, and may add noise to the results. Here, we investigate the sensitivity of entropy-based similarity measures to noise from uninformative smoothing. Our experiments with two subcategorization acquisition systems show that similarity measures vary in their robustness. While some are led astray by noise from smoothing, others are more resilient.