A statistical approach to machine translation
Computational Linguistics
Evaluating sense disambiguation across diverse parameter spaces
Natural Language Engineering
The role of domain information in Word Sense Disambiguation
Natural Language Engineering
Word sense ambiguation: clustering related senses
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Word-sense disambiguation using statistical models of Roget's categories trained on large corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Scaling to very very large corpora for natural language disambiguation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
A noisy-channel model for document compression
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A noisy-channel approach to question answering
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
An improved error model for noisy channel spelling correction
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Learning semantic classes for word sense disambiguation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Meaningful clustering of senses helps boost word sense disambiguation performance
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
Smoothing a tera-word language model
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Broad-coverage sense disambiguation and information extraction with a supersense sequence tagger
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Proceedings of the 4th International Workshop on Semantic Evaluations
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
KU: word sense disambiguation by substitution
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
USYD: WSD and lexical substitution using the Web1T corpus
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
On the use of automatically acquired examples for all-nouns word sense disambiguation
Journal of Artificial Intelligence Research
Optimizing classifier performance in word sense disambiguation by redefining word sense classes
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Improving WSD with multi-level view of context monitored by similarity measure
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Bilingual sense similarity for statistical machine translation
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
From frequency to meaning: vector space models of semantics
Journal of Artificial Intelligence Research
Unsupervised part of speech tagging using unambiguous substitutes from a statistical language model
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Compositional expectation: a purely distributional model of compositional semantics
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Learning a taxonomy from a set of text documents
Applied Soft Computing
A quick tour of word sense disambiguation, induction and related approaches
SOFSEM'12 Proceedings of the 38th international conference on Current Trends in Theory and Practice of Computer Science
Word sense disambiguation as a traveling salesman problem
Artificial Intelligence Review
Hi-index | 0.00 |
We introduce a generative probabilistic model, the noisy channel model, for unsupervised word sense disambiguation. In our model, each context C is modeled as a distinct channel through which the speaker intends to transmit a particular meaning S using a possibly ambiguous word W. To reconstruct the intended meaning the hearer uses the distribution of possible meanings in the given context P(S|C) and possible words that can express each meaning P(W|S). We assume P(W|S) is independent of the context and estimate it using WordNet sense frequencies. The main problem of unsupervised WSD is estimating context-dependent P(S|C) without access to any sense-tagged text. We show one way to solve this problem using a statistical language model based on large amounts of untagged text. Our model uses coarse-grained semantic classes for S internally and we explore the effect of using different levels of granularity on WSD performance. The system outputs fine-grained senses for evaluation, and its performance on noun disambiguation is better than most previously reported unsupervised systems and close to the best supervised systems.