Detecting Semantic Relations between Named Entities in Text Using Contextual Features

This paper proposes a supervised learning method for detecting a semantic relation between a given pair of named entities, which may be located in different sentences. The method employs newly introduced contextual features based on centering theory as well as conventional syntactic and word-based features. These features are organized as a tree structure and are fed into a boosting-based classification algorithm. Experimental results show the proposed method outperformed prior methods, and increased precision and recall by 4.4% and 6.7%.

• Type: Conference
• Year: 2007
• Where: ACL
• Authors: Toru Hirano, Yoshihiro Matsuo, Gen-ichiro Kikui
• Keywords: ACL 2007, Computational Linguistics, Contextual Features, Supervised Learning Method, Word-based Features