Identifying semantic equivalence for multi-document summarisation
Artificial Intelligence Review
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
Textual entailment recognition using a linguistically–motivated decision tree classifier
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
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The last few years have seen a surge in interest in modeling techniques aimed at measuring semantic equivalence and entailment, with work on paraphrase acquisition/generation, WordNetbased expansion, distributional similarity, supervised learning of semantic variability in information extraction, and the identification of patterns in template-based QA. Being able to identify when two strings "mean the same thing" or that one entails the other are crucial abilities for a broad range of NLP-related applications, ranging from question answering to summarization. These proceedings contain a rich variety of papers centered on the problem of modeling semantic overlap between linguistic strings. This is a difficult problem space, encompassing issues of lexical choice, syntactic alternation, semantic inference, and reference/discourse structure.