关键词: 命名实体识别, 链接数据, DBpedia Spotlight

Abstract: The task of named entity recognition can bridge the gap between knowledge bases and nature languages, and support the research work in keyword extraction, machine translation, topic detection and tracking, etc. Based on the analysis of current research in the field of named entity recognition, this paper proposes a general-purpose optimization scheme for named entity recognition. Firstly, this paper designs and implements an incremental extending method, by using a candidate set, which can reduce the dependency on the training set. Secondly, by leveraging the concept of pointwise mutual information ratio, this paper effectively makes feature selection on the contexts of entities, which may reduce the context space significantly and meanwhile improve the performance of annotation results. Finally, this paper presents the secondary disambiguation method based on topic vectors, which can further enhance the precision of annotation. This paper conducts extensive comparison experiments on the widely-used open-source named entity recognition system DBpedia Spotlight. It has been verified that the proposed optimization scheme outperforms the state-of-the-art methods.

Key words: named entity recognition, linked data, DBpedia Spotlight