PNNL: a supervised maximum entropy approach to word sense disambiguation

Authors:
Stephen Tratz;Antonio Sanfilippo;Michelle Gregory;Alan Chappell;Christian Posse;Paul Whitney
Affiliations:
Pacific Northwest National Laboratory, Richland, WA;Pacific Northwest National Laboratory, Richland, WA;Pacific Northwest National Laboratory, Richland, WA;Pacific Northwest National Laboratory, Richland, WA;Pacific Northwest National Laboratory, Richland, WA;Pacific Northwest National Laboratory, Richland, WA
Venue:
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Year:
2007

Citing 7
Cited 9

A maximum entropy approach to natural language processing

Computational Linguistics
A Comparative Study on Feature Selection in Text Categorization

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
An extensive empirical study of feature selection metrics for text classification

The Journal of Machine Learning Research
A non-projective dependency parser

ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Building a sense tagged corpus with open mind word expert

WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
Learning semantic classes for word sense disambiguation

ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
The role of semantic roles in disambiguating verb senses

ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics

Word sense disambiguation: A survey

ACM Computing Surveys (CSUR)
Semi-supervised Word Sense Disambiguation Using the Web as Corpus

CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
OntoNotes: corpus cleanup of mistaken agreement using word sense disambiguation

COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Word sense disambiguation using OntoNotes: an empirical study

EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Disambiguation of preposition sense using linguistically motivated features

SRWS '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium
A knowledge based approach for capturing rich semantic representations from text for intelligent systems

International Journal of Advanced Intelligence Paradigms
A Lexicographic Encoding for Word Sense Disambiguation with Evolutionary Neural Networks

AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
ISI: Automatic classification of relations between nominals using a maximum entropy classifier

SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
The latent words language model

Computer Speech and Language

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we described the PNNL Word Sense Disambiguation system as applied to the English all-word task in SemEval 2007. We use a supervised learning approach, employing a large number of features and using Information Gain for dimension reduction. The rich feature set combined with a Maximum Entropy classifier produces results that are significantly better than baseline and are the highest F-score for the fined-grained English all-words subtask of SemEval.