Recognizing Compound Entity Phrases In Hybrid Academic Domains In View Of Community Division

Classifying compound named entities in academic domains, such as the name of papers, patents and projects, plays an important role in enhancing many applications such as knowledge discovering and intelligence property protection. However, there are very little work on this novel and hard problem. Prior mainstream approaches mainly focus on classifying basic named entities (e.g. person names, organization names, twitter named entities, and simple entities in specific sci-tech domain etc). We use context templates to extract the possible candidate compound entities roughly, which is used for reducing searching space of text splitting. We reduce the text splitting problem to the community division problem, which is addressed based on the dynamic programming strategy. The construction of indicative words set used in segment validating is reduced to the classical minimum set cover problem, which is also addressed based on dynamic programming. Experimental results on classifying real-world science technology compound entities show that GenericSegVal achieves a sharp increase in both precision rate and recall rate by comparing with the supervised bidirectional LSTM approach. (C) 2017 The Authors. Published by Elsevier B.V.