Abstract
Finding paraphrases in text is an important task with implications for generation, summarization and question answering, among other applications. Of particular interest to those applications is the specific formulation of the task where the paraphrases are templated, which provides an easy way to lexicalize one message in multiple ways by simply plugging in the relevant entities. Previous work has focused on mining paraphrases from parallel and comparable corpora, or mining very short sub-sentence synonyms and paraphrases. In this paper we present an approach which combines distributional and KB-driven methods to allow robust mining of sentence-level paraphrasai templates, utilizing a rich type system for the slots, from a plain text corpus.
Cite
CITATION STYLE
Biran, O., Blevins, T., & McKeown, K. (2016). Mining paraphrasai typed templates from a plain text corpus. In 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers (Vol. 4, pp. 1913–1923). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/p16-1180
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