TY - JOUR
T1 - Leveraging unannotated texts for scientific relation extraction
AU - Dai, Qin
AU - Inoue, Naoya
AU - Reisert, Paul
AU - Inui, Kentaro
N1 - Funding Information:
This work was supported by JST CREST Grant Number JPMJCR1513, Japan and KAKENHI Grant Number 16H06614.
Publisher Copyright:
Copyright © 2018 The Institute of Electronics, Information and Communication Engineers
PY - 2018/12
Y1 - 2018/12
N2 - A tremendous amount of knowledge is present in the ever-growing scientific literature. In order to efficiently grasp such knowledge, various computational tasks are proposed that train machines to read and analyze scientific documents. One of these tasks, Scientific Relation Extraction, aims at automatically capturing scientific semantic relationships among entities in scientific documents. Conventionally, only a limited number of commonly used knowledge bases, such as Wikipedia, are used as a source of background knowledge for relation extraction. In this work, we hypothesize that unannotated scientific papers could also be utilized as a source of external background information for relation extraction. Based on our hypothesis, we propose a model that is capable of extracting background information from unannotated scientific papers. Our experiments on the RANIS corpus [1] prove the effectiveness of the proposed model on relation extraction from scientific articles.
AB - A tremendous amount of knowledge is present in the ever-growing scientific literature. In order to efficiently grasp such knowledge, various computational tasks are proposed that train machines to read and analyze scientific documents. One of these tasks, Scientific Relation Extraction, aims at automatically capturing scientific semantic relationships among entities in scientific documents. Conventionally, only a limited number of commonly used knowledge bases, such as Wikipedia, are used as a source of background knowledge for relation extraction. In this work, we hypothesize that unannotated scientific papers could also be utilized as a source of external background information for relation extraction. Based on our hypothesis, we propose a model that is capable of extracting background information from unannotated scientific papers. Our experiments on the RANIS corpus [1] prove the effectiveness of the proposed model on relation extraction from scientific articles.
KW - Relation extraction
KW - Scientific document
KW - Semantically related word
KW - Word embedding
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U2 - 10.1587/transinf.2018EDP7180
DO - 10.1587/transinf.2018EDP7180
M3 - Article
AN - SCOPUS:85057561455
SN - 0916-8532
VL - E101D
SP - 3209
EP - 3217
JO - IEICE Transactions on Information and Systems
JF - IEICE Transactions on Information and Systems
IS - 12
ER -