Natural language parsing as statistical pattern recognition
Natural language parsing as statistical pattern recognition
WordNet: a lexical database for English
Communications of the ACM
DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Extracting paraphrases from a parallel corpus
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Extracting structural paraphrases from aligned monolingual corpora
PARAPHRASE '03 Proceedings of the second international workshop on Paraphrasing - Volume 16
Unsupervised construction of large paraphrase corpora: exploiting massively parallel news sources
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Corpus-based and knowledge-based measures of text semantic similarity
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Paraphrase recognition via dissimilarity significance classification
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Paraphrase identification on the basis of supervised machine learning techniques
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
Assigning function tags with a simple model
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
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We show in this article how an approach developed for the task of recognizing textual entailment relations can be extended to identify paraphrase and elaboration relations. Entailment is a unidirectional relation between two sentences in which one sentence logically infers the other. There seems to be a close relation between entailment and two other sentence-to-sentence relations: elaboration and paraphrase. This close relation is discussed to theoretically justify the newly derived approaches. The proposed approaches use lexical, syntactic, and shallow negation handling. The proposed approaches offer significantly better results than several baselines. When compared to other paraphrase and elaboration approaches they produce similar or better results. We report results on several data sets: the Microsoft Research Paraphrase corpus, a benchmark for evaluating approaches to paraphrase identification, and a data set collected from high-school students' interactions with an intelligent tutoring system iSTART, which includes both paraphrase and elaboration utterances.