Adding Context to Semantic Data-Driven Paraphrasing
Vered Shwartz, Ido Dagan · 2016
Recognizing lexical inferences between pairs of terms is a common task in NLP applications, which should typically be performed within a given context. Such context-sensitive inferences have to consider both term meaning in context as well as the fine-grained relation holding between the terms. Hence, to develop suitable lexical inference methods, we need datasets that are annotated with fine-grained semantic relations in-context. Since existing datasets either provide outof-context annotations or refer to coarsegrained relations, we propose a methodology for adding context-sensitive annotations. We demonstrate our methodology by applying it to phrase pairs from PPDB 2.0, creating a novel dataset of finegrained lexical inferences in-context and showing its utility in developing contextsensitive methods.