基于树核函数的实体语义关系抽取方法研究

Research on Tree Kernel-Based Entity Semantic Relation Extraction

该文描述了一种改进的基于树核函数的实体语义关系抽取方法,通过在原有关系实例的结构化信息中加入实体语义信息和去除冗余信息的方法来提高关系抽取的性能。该方法在最短路径包含树的基础上,首先加入实体类型、引用类型等与实体相关的语义信息,然后对树进行裁剪,去掉修饰语冗余和并列冗余信息,并扩充所有格结构,最后生成实体语义关系实例。在ACE RDC 2004基准语料上进行的关系检测和7个关系大类抽取的实验表明,该方法在较大程度上提高了实体语义关系识别和分类的效果,F值分别达到了79.1%和71.9%。

This paper describes an improved tree kernel-based approach to entity semantic relation extraction, where the performance is improved by incorporation of entity-related semantic information into, the structured representation of relation instances and the pruning of redundant information. Starting from the Shortest Path-enclosed Tree for a relation instance, entity-relation semantic information, such as entity types, subtypes, and mention types etc., are first uniformly appended. Then modifications to noun phrases and redundant information in conjunction coordination structures are removed away, but the possessive structure is further included. With such generated appropriate representation of the relation instance, experiments on the ACE RDC 2004 benchmark corpus shows that our method significantly improves the performance, achieving the F-measure of 79.1% and 71.9 % on the task of relation detection and top-level relation extraction respectively.