Discovery of inference rules for question-answering
Natural Language Engineering
Extracting paraphrases from a parallel corpus
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Logic form transformation of WordNet and its applicability to question answering
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
A common theory of information fusion from multiple text sources step one: cross-document structure
SIGDIAL '00 Proceedings of the 1st SIGdial workshop on Discourse and dialogue - Volume 10
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Paraphrase identification using machine learning techniques
ICNVS'10 Proceedings of the 12th international conference on Networking, VLSI and signal processing
Textual entailment beyond semantic similarity information
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Paraphrase identification on the basis of supervised machine learning techniques
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
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A fundamental phenomenon in Natural Language Processing concerns the semantic variability of expressions. Identifying that two texts express the same meaning with different words is a challenging problem. We discuss the role of entailment for various Natural Language Processing applications and develop a machine learning system for their resolution. In our system, text similarity is based on the number of consecutive and non-consecutive word overlaps between two texts. The system is language and resource independent, as it does not use external knowledge resources such as WordNet, thesaurus, semantic, syntactic or part-of-speech tagging tools. In this paper all tests were done for English, but our system can be used with no restrains by other languages.