An Information-Theoretic Definition of Similarity
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Extracting paraphrases from a parallel corpus
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
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Automatic evaluation of summaries using N-gram co-occurrence statistics
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Assessing system agreement and instance difficulty in the lexical sample tasks of SENSEVAL-2
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
Unsupervised construction of large paraphrase corpora: exploiting massively parallel news sources
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Mavuno: a scalable and effective Hadoop-based paraphrase acquisition system
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Re-examining machine translation metrics for paraphrase identification
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Terminological paraphrase extraction from scientific literature based on predicate argument tuples
Journal of Information Science
Similarity measures based on latent dirichlet allocation
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Paraphrase acquisition via crowdsourcing and machine learning
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
Experiments with semantic similarity measures based on LDA and LSA
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
Exploiting discourse information to identify paraphrases
Expert Systems with Applications: An International Journal
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This paper presents a machine learning approach for paraphrase identification which uses lexical and semantic similarity information. In the experimental studies, we examine the limitations of the designed attributes and the behavior of three machine learning classifiers. With the objective to increase the final performance of the system, we scrutinize the influence of the combination of lexical and semantic information, as well as techniques for classifier combination.