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. Relation feature selection and extraction are two key issues. The location features possess excellent computability and operability, and the semantic features have strong intelligibility and reality. Currently, relation extraction of Chinese named entities mainly adopts the vector space model (VSM) or a traditional semantic computing method, and these two methods use either the location features or the semantic features only, resulting in unsatisfactory extraction. To improve the extraction results, we propose a method that combines the information gain of the positions of words and the semantic computing based on HowNet to extract Chinese named entity relations, and present a relation extraction method of Chinese named entities, called LSE, which is scalable, semi-supervised and domain independent. Extensive experiments have been performed to show that our approach is superior, with an F-score of 0.881, which is at least 0.115 better than existing extraction methods that use either the location features or the semantic features.