Semantic retrieval for the accurate identification of relational concepts in massive textbases

Authors:
Yusuke Miyao;Tomoko Ohta;Katsuya Masuda;Yoshimasa Tsuruoka;Kazuhiro Yoshida;Takashi Ninomiya;Jun'ichi Tsujii
Affiliations:
University of Tokyo;University of Tokyo;University of Tokyo;University of Manchester;University of Tokyo;University of Tokyo, Bunkyo-ku, Tokyo Japan;University of Tokyo and University of Manchester
Venue:
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Year:
2006

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Abstract

This paper introduces a novel framework for the accurate retrieval of relational concepts from huge texts. Prior to retrieval, all sentences are annotated with predicate argument structures and ontological identifiers by applying a deep parser and a term recognizer. During the run time, user requests are converted into queries of region algebra on these annotations. Structural matching with pre-computed semantic annotations establishes the accurate and efficient retrieval of relational concepts. This framework was applied to a text retrieval system for MEDLINE. Experiments on the retrieval of biomedical correlations revealed that the cost is sufficiently small for real-time applications and that the retrieval precision is significantly improved.