Nhung T H Nguyen1, Makoto Miwa2, Yoshimasa Tsuruoka3, Takashi Chikayama4, Satoshi Tojo5. 1. School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan. nthnhung@jaist.ac.jp. 2. Toyota Technological Institute, Nagoya, Japan. makoto-miwa@toyota-ti.ac.jp. 3. Graduate School of Engineering, The University of Tokyo, Tokyo, Japan. tsuruoka@logos.t.u-tokyo.ac.jp. 4. Graduate School of Engineering, The University of Tokyo, Tokyo, Japan. chikayama@logos.t.u-tokyo.ac.jp. 5. School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan. tojo@jaist.ac.jp.
Abstract
BACKGROUND: Relation extraction is a fundamental technology in biomedical text mining. Most of the previous studies on relation extraction from biomedical literature have focused on specific or predefined types of relations, which inherently limits the types of the extracted relations. With the aim of fully leveraging the knowledge described in the literature, we address much broader types of semantic relations using a single extraction framework. RESULTS: Our system, which we name PASMED, extracts diverse types of binary relations from biomedical literature using deep syntactic patterns. Our experimental results demonstrate that it achieves a level of recall considerably higher than the state of the art, while maintaining reasonable precision. We have then applied PASMED to the whole MEDLINE corpus and extracted more than 137 million semantic relations. The extracted relations provide a quantitative understanding of what kinds of semantic relations are actually described in MEDLINE and can be ultimately extracted by (possibly type-specific) relation extraction systems. CONCLUSION: PASMED extracts a large number of relations that have previously been missed by existing text mining systems. The entire collection of the relations extracted from MEDLINE is publicly available in machine-readable form, so that it can serve as a potential knowledge base for high-level text-mining applications.
BACKGROUND: Relation extraction is a fundamental technology in biomedical text mining. Most of the previous studies on relation extraction from biomedical literature have focused on specific or predefined types of relations, which inherently limits the types of the extracted relations. With the aim of fully leveraging the knowledge described in the literature, we address much broader types of semantic relations using a single extraction framework. RESULTS: Our system, which we name PASMED, extracts diverse types of binary relations from biomedical literature using deep syntactic patterns. Our experimental results demonstrate that it achieves a level of recall considerably higher than the state of the art, while maintaining reasonable precision. We have then applied PASMED to the whole MEDLINE corpus and extracted more than 137 million semantic relations. The extracted relations provide a quantitative understanding of what kinds of semantic relations are actually described in MEDLINE and can be ultimately extracted by (possibly type-specific) relation extraction systems. CONCLUSION:PASMED extracts a large number of relations that have previously been missed by existing text mining systems. The entire collection of the relations extracted from MEDLINE is publicly available in machine-readable form, so that it can serve as a potential knowledge base for high-level text-mining applications.
Authors: Nikolai Daraselia; Anton Yuryev; Sergei Egorov; Svetalana Novichkova; Alexander Nikitin; Ilya Mazo Journal: Bioinformatics Date: 2004-01-22 Impact factor: 6.937
Authors: Sofie Van Landeghem; Jari Björne; Chih-Hsuan Wei; Kai Hakala; Sampo Pyysalo; Sophia Ananiadou; Hung-Yu Kao; Zhiyong Lu; Tapio Salakoski; Yves Van de Peer; Filip Ginter Journal: PLoS One Date: 2013-04-17 Impact factor: 3.240