A Winnow-Based Approach to Context-Sensitive Spelling Correction
Machine Learning - Special issue on natural language learning
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
Discovery of inference rules for question-answering
Natural Language Engineering
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Principle-based parsing without overgeneration
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Extracting paraphrases from a parallel corpus
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Learning to paraphrase: an unsupervised approach using multiple-sequence alignment
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
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
Direct word sense matching for lexical substitution
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Paraphrasing for automatic evaluation
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
An inference model for semantic entailment in natural language
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Local Rephrasing Suggestions for Supporing the Work of Writers
GoTAL '08 Proceedings of the 6th international conference on Advances in Natural Language Processing
Global learning of focused entailment graphs
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Topic models for meaning similarity in context
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
A supervised method of feature weighting for measuring semantic relatedness
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
Web-based validation for contextual targeted paraphrasing
MTTG '11 Proceedings of the Workshop on Monolingual Text-To-Text Generation
Learning entailment relations by global graph structure optimization
Computational Linguistics
Classification-based contextual preferences
TIWTE '11 Proceedings of the TextInfer 2011 Workshop on Textual Entailment
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Lexical paraphrasing is an inherently context sensitive problem because a word's meaning depends on context. Most paraphrasing work finds patterns and templates that can replace other patterns or templates in somecontext, but we are attempting to make decisions for a specificcontext. In this paper we develop a global classifier that takes a word vand its context, along with a candidate word u, and determines whether ucan replace vin the given context while maintaining the original meaning.We develop an unsupervised, bootstrapped, learning approach to this problem. Key to our approach is the use of a very large amount of unlabeled data to derive a reliable supervision signal that is then used to train a supervised learning algorithm. We demonstrate that our approach performs significantly better than state-of-the-art paraphrasing approaches, and generalizes well to unseen pairs of words.