• 数据库技术 •

谓词概念连通度的中文实体关系抽取策略

1. 1. 江西财经大学 信息管理学院，南昌 330032
2. 江西中医药大学 计算机学院，南昌 330004
3. 江西财经大学 软件与通信工程学院，南昌 330032
• 出版日期:2014-11-01 发布日期:2014-11-04

Strategy of Extracting Chinese Entities Relation Based on Predicate Concept Connectivity

XIA Jiali1, CHENG Chunlei1,2+, CHEN Hui3, CAO Zhonghua1,3, LI Guangquan1

1. 1. School of Information Management, Jiangxi University of Finance and Economics, Nanchang 330032, China
2. School of Computer Science, Jiangxi University of Traditional Chinese Medicine, Nanchang 330004, China
3. School of Software & Communication Engineering, Jiangxi University of Finance and Economics, Nanchang 330032, China
• Online:2014-11-01 Published:2014-11-04

Abstract: Chinese entities relation extraction task is a research focus of text retrieval and knowledge discovery in the open corpus. In the traditional extraction strategies, there exist some problems such as heavy workload of manual annotating, poor pattern versatility and relatively fixed relational granularity, etc. All these restrict the extraction effect in open corpus especially. This paper builds the predicate concept model (PCM) relying on hierarchical structure and relational connectivity of concept, proposes the predicate concept acquisition strategy for incremental concept learning (PCIA), achieves the extraction strategy based on predicate concept connectivity (PCCS), and carries out the untight, long-distant relation extraction ultimately. The construction of the formal concepts is relatively independent, and the combination of concept granularities is more flexible. Therefore, the description approach of the relationship has a better versatility and interpretability, and provides an effective means for unknown relationship identifying and extracting in the open corpus. The experimental results show that PCCS improves the effect of entities identification and entities connectivity path choice, and obtains good entities relation extracting performance.