Research of Semantic Role Labeling and Application in Patent Knowledge Extraction.
Ling'en Meng, Yanqing He, Ying Li · 2014
Semantic Role Labeling (SRL) is a leading task of identifying arguments for a predicate and assigning semantically meaningful labels to them. SRL is crucial to information extraction, question answering, and machine translation. When applied to patent text, existing tools for SRL have unsatisfying performance because of long sentences. To improve performance in patent SRL systems, this study separates each sentence in patent abstracts into a simpler structure, and then labels semantic roles for the simplified sentence. At last, semantic information and semantic framework for frequently used words are used to extract patent knowledge. Our work demonstrates that the method used in this article can improve the performance in SRL system and obtain beneficial knowledge from patents.