An Approach to News Paraphrase Recognition Based on SRL

An Approach to News Paraphrase Recognition Based on SRL Xiaofeng Wu, Chengqing Zong (National Lab of Pattern Recognition, Institute of Automation, CAS, Beijing 100190, China) Abstract: Paraphrase Recognition can be regarded as a sub-problem of Text Entailment Recognition. This problem is hard in that simply using term frequency or syntax information is prone to error judgment. For even the same pack of words can cook up sentences with totally different meanings, while similar parsing trees can either have different meanings. In this paper we present a new approach based on Semantic Role Labeling (SRL) to identify paraphrase. In our approach, we first label sentences with semantic role, then we get features that can partly represent the meaning of the sentence. By doing so, we also take the specialty of News sentences under consideration. Our experiment proved the effectiveness of our approach.