A survey of paraphrasing and textual entailment methods
Journal of Artificial Intelligence Research
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Applied textual inference has attracted a significant amount of attention in recent years. Recognizing textual entailments and detecting semantic equivalences between texts are at the core of many NLP tasks, including question answering, information extraction, text summarization, and many others. Developing generic algorithms and resources for inference and paraphrasing would therefore be applicable to a broad range of NLP applications. The success of the first three Recognizing Textual Entailment (RTE) Pascal challenges and the high participation in this year's NIST-organized RTE challenge show that there is a very substantial interest in the area among the research community. RTE and paraphrase detection tasks have considerably stimulated research in the area of applied semantics, and computational models for textual inference are becoming more and more reliable and accurate as a result. The goal of this workshop is to provide a common forum where people can discuss and compare novel ideas, models and tools for textual inference and paraphrasing. The workshop follows previous ACL workshops on these topics (the ACL workshop on "Empirical Modeling of Semantic Equivalence and Entailment", 2005, and the joint ACL-PASCAL workshop "Textual Entailment and Paraphrasing", 2007). This line of workshops goes in parallel with the RTE challenges, now organized by NIST, by promoting a deeper understanding of what are the scientific achievements and the new findings emerging in the field.