Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Summarization beyond sentence extraction: a probabilistic approach to sentence compression
Artificial Intelligence
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Discriminative Reranking for Natural Language Parsing
Computational Linguistics
Paraphrasing with bilingual parallel corpora
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Improved statistical machine translation using paraphrases
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of 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
Re-evaluating machine translation results with paraphrase support
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Syntactic constraints on paraphrases extracted from parallel corpora
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Answering the question you wish they had asked: the impact of paraphrasing for question answering
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Global inference for sentence compression an integer linear programming approach
Journal of Artificial Intelligence Research
Using paraphrases for parameter tuning in statistical machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Extracting paraphrase patterns from bilingual parallel corpora
Natural Language Engineering
Paraphrase recognition using machine learning to combine similarity measures
ACLstudent '09 Proceedings of the ACL-IJCNLP 2009 Student Research Workshop
Robust machine translation evaluation with entailment features
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Source-language entailment modeling for translating unknown terms
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Application-driven statistical paraphrase generation
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Improved statistical machine translation using monolingually-derived paraphrases
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Statistical Machine Translation
Statistical Machine Translation
Hitting the right paraphrases in good time
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Leveraging multiple MT engines for paraphrase generation
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
A survey of paraphrasing and textual entailment methods
Journal of Artificial Intelligence Research
Generating phrasal and sentential paraphrases: A survey of data-driven methods
Computational Linguistics
Query rewriting using monolingual statistical machine translation
Computational Linguistics
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We present a method that paraphrases a given sentence by first generating candidate paraphrases and then ranking (or classifying) them. The candidates are generated by applying existing paraphrasing rules extracted from parallel corpora. The ranking component considers not only the overall quality of the rules that produced each candidate, but also the extent to which they preserve grammaticality and meaning in the particular context of the input sentence, as well as the degree to which the candidate differs from the input. We experimented with both a Maximum Entropy classifier and an SVR ranker. Experimental results show that incorporating features from an existing paraphrase recognizer in the ranking component improves performance, and that our overall method compares well against a state of the art paraphrase generator, when paraphrasing rules apply to the input sentences. We also propose a new methodology to evaluate the ranking components of generate-and-rank paraphrase generators, which evaluates them across different combinations of weights for grammaticality, meaning preservation, and diversity. The paper is accompanied by a paraphrasing dataset we constructed for evaluations of this kind.