A technique for computer detection and correction of spelling errors
Communications of the ACM
Discovery of inference rules for question-answering
Natural Language Engineering
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
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Learning entailment rules for unary templates
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Syntactic constraints on paraphrases extracted from parallel corpora
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Extracting paraphrase patterns from bilingual parallel corpora
Natural Language Engineering
For the sake of simplicity: unsupervised extraction of lexical simplifications from Wikipedia
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Generating entailment rules from FrameNet
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
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Recent work on Textual Entailment has shown a crucial role of knowledge to support entailment inferences. However, it has also been demonstrated that currently available entailment rules are still far from being optimal. We propose a methodology for the automatic acquisition of large scale context-rich entailment rules from Wikipedia revisions, taking advantage of the syntactic structure of entailment pairs to define the more appropriate linguistic constraints for the rule to be successfully applicable. We report on rule acquisition experiments on Wikipedia, showing that it enables the creation of an innovative (i.e. acquired rules are not present in other available resources) and good quality rule repository.