Knowledge element relation extraction using conditional random fields

Knowledge element relation extraction is to find predefined relations between pairs of knowledge elements from text documents. As a novel form for organization and management of knowledge resources, knowledge element relation can be utilized to establish knowledge navigation system, knowledge retrieval system and collaborative knowledge construction system. In this paper, we employ conditional random fields (CRFs) to extract relations between knowledge elements from natural language documents by treating the relation extraction task as a sequence labeling problem. We first introduce three rules to generate candidate relation instances, and then incorporate various features including terms, semantic type, distance and context information to represent candidate relation instances. Experimental evaluation shows that our method achieves better performance than previous work. It also indicates that CRFs outperform other probabilistic models i.e. hidden Markov model and maximum entropy, and show effective in knowledge element relation extraction.

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