A relation extraction method of Chinese named entities based on location and semantic features

Named entity relations are a foundation of semantic networks, ontology and the semantic Web, and are widely used in information retrieval and machine translation, as well as automatic question and answering systems. In named entity relations, relational feature selection and extraction are two key issues. The location features possess excellent computability and operability, while the semantic features have strong intelligibility and reality. Currently, relation extraction of Chinese named entities mainly adopts the Vector Space Model (VSM), a traditional semantic computing or the classification method, and these three methods use either the location features or the semantic features alone, resulting in unsatisfactory extraction. A relation extraction method of Chinese named entities called LaSE is proposed to combine the information gain of the positions of words and semantic computing based on HowNet. LaSE is scalable, semi-supervised and domain independent. Extensive experiments show that LaSE is superior, with an F-score of 0.879, which is at least 0.113 better than existing extraction methods that use either the location features or the semantic features alone.