SemKer: Syntactic/Semantic Kernels for Recognizing Textual Entailment

  title={SemKer: Syntactic/Semantic Kernels for Recognizing Textual Entailment},
  author={Yashar Mehdad and Alessandro Moschitti and Fabio Massimo Zanzotto},
In this paper we describe the SemKer system participating to the fifth Recognizing of Textual Entailment (RTE5) challenge. The major novelty with respect to the systems with which we participated to the previous challenges is the use of semantic knowledge based on Wikipedia. More specifically, we used it to enrich the similarity measure between pairs of text and hypothesis (i.e. the tree kernel for text and hypothesis pairs), with a lexical similarity (i.e. the similarity between the leaves of… CONTINUE READING