关键词: 命名实体消歧, 领域, 依赖图, 近似算法

Abstract: Named Entity Disambiguation（NED） maps ambiguous mentions in natural language texts to corresponding entities in the knowledge base. Most of current NED techniques utilize collective disambiguation to harness more semantic information and achieve higher precision, whereas suffer from relatively lower efficiency. In this paper, a domain-oriented NED method is proposed, which introduces the concept of domain to enrich the set of features, and a mention-entity dependency graph is constructed by using the features, so as to achieve collective disambiguation. When building the dependency graph, based on existing construction methods, extra links are established between mentions by utilizing the relations among mentions. As a result, the structure of dependency graph can be optimized, and the processing order of the algorithm is also improved indirectly. Experimental results on real-life benchmark datasets show that this method is more efficient and accurate than other methods of the kind.

Key words: named entity disambiguation, domain, dependency graph, approximation algorithm