PKDE4J: Entity and relation extraction for public knowledge discovery.
Due to an enormous number of scientific publications that cannot be handled manually, there is a rising interest in text-mining techniques for automated information extraction, especially in the biomedical field. Such techniques provide effective means of information search, knowledge discovery, and hypothesis generation. Most previous studies have primarily focused on the design and performance improvement of either named entity recognition or relation extraction. In this paper, we present PKDE4J, a comprehensive text-mining system that integrates dictionary-based entity extraction and rule-based relation extraction in a highly flexible and extensible framework. Starting with the Stanford CoreNLP, we developed the system to cope with multiple types of entities and relations. The system also has fairly good performance in terms of accuracy as well as the ability to configure text-processing components. We demonstrate its competitive performance by evaluating it on many corpora and found that it surpasses existing systems with average F-measures of 85% for entity extraction and 81% for relation extraction.
Department of Library and Information Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Republic of Korea. Electronic address: min.song@yonsei.ac.kr.
Min
M
Song
Department of Library and Information Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Republic of Korea. Electronic address: krevas@yonsei.ac.kr.
Won Chul
WC
Kim
Department of Library and Information Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Republic of Korea. Electronic address: leedahee@yonsei.ac.kr.
Dahee
D
Lee
Department of Library and Information Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Republic of Korea. Electronic address: goeun.heo@yonsei.ac.kr.
Go Eun
GE
Heo
Department of Library and Information Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Republic of Korea. Electronic address: ky.kang@yonsei.ac.kr.
Keun Young
KY
Kang
D057225:Data Mining; D019359:Knowledge; D063369:Knowledge Discovery; D010506:Periodicals as Topic; D011642:Publications
Information extraction; Named entity recognition; Relation extraction; Text mining