Relation Extraction Using Support Vector Machine

This paper presents a supervised approach for relation extraction. We apply Support Vector Machines to detect and classify the relations in Automatic Content Extraction (ACE) corpus. We use a set of features including lexical tokens, syntactic structures, and semantic entity types for relation detection and classification problem. Besides these linguistic features, we successfully utilize the distance between two entities to improve the performance. In relation detection, we filter out the negative relation candidates using entity distance threshold. In relation classification, we use the entity distance as a feature for Support Vector Classifier. The system is evaluated in terms of recall, precision, and Fmeasure, and errors of the system are analyzed with proposed solution.

• Type: Conference
• Year: 2005
• Where: IJCNLP
• Authors: Gum-Won Hong
• Keywords: Entity Distance, IJCNLP 2005, Natural Language Processing, Relation Detection, Support Vector