融合多种特征的实体链接技术研究

实体消歧是自然语言理解的重要研究内容,旨在解决文本信息中普遍存在的命名实体歧义问题,在信息抽取、知识工程和语义网络等领域有广泛的应用价值。实体链接是实体消歧的一种重要方法,该方法将具有歧义的实体指称项链接到给定的知识库中从而实现实体歧义的消除^1]。传统的实体链接方法主要利用上下文的词语匹配等表层特征,缺乏深层语义信息,针对这一问题,该文提出的实体链接方法利用了多种特征,从不同的维度捕获语义信息。为了更好地融合各个维度的特征,该文利用了基于排序学习框架的实体链接方法,与传统的方法相比,节省了人工对大量的模型参数选择和调节的工作,与基于分类的方法相比,能更好地利用到候选之间的关系信息。在TAC-KBP-2009的实体链接评测数据上的实验表明,该文提出的特征和方法表现出良好的性能,在评测指标上高出参赛队伍最好水平2.21%,达到84.38%。

Entity Linking Based on Multiple Features

Entity linking is an important method of entity disambiguation, which aims to map an entity to an entry stored in the existing knowledge base. Several methods have been proposed to tackle this problem, most of which are based on the co-occurrence statistics without capture various semantic relations. In this paper, we make use of multiple features and propose a learning to rank algorithm for entity linking. It effectively utilizes the relationship information among the candidates and save a lot of time and effort. The experiment results on the TAC KBP 2009 dataset demonstrate the effectiveness of our proposed features and framework by an accuracy of 84.38%, exceeding the best result of the TAC KBP 2009 by 2.21%.