Leveraging Unannotated Texts for Scientific Relation Extraction

Qin DAI; Naoya INOUE; Paul REISERT; Kentaro INUI

doi:10.1587/transinf.2018EDP7180

Leveraging Unannotated Texts for Scientific Relation Extraction

Qin DAI, Naoya INOUE, Paul REISERT, Kentaro INUI

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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.

Publication: IEICE TRANSACTIONS on Information Vol.E101-D No.12 pp.3209-3217

Publication Date: 2018/12/01

Publicized: 2018/09/14

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2018EDP7180

Type of Manuscript: PAPER

Category: Natural Language Processing

Authors

Qin DAI
  Tohoku University
Naoya INOUE
  Tohoku University,RIKEN Center for Advanced Intelligence Project
Paul REISERT
  RIKEN Center for Advanced Intelligence Project
Kentaro INUI
  Tohoku University,RIKEN Center for Advanced Intelligence Project

Keyword

relation extraction, scientific document, word embedding, semantically related word

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Qin DAI, Naoya INOUE, Paul REISERT, Kentaro INUI, "Leveraging Unannotated Texts for Scientific Relation Extraction" in IEICE TRANSACTIONS on Information, vol. E101-D, no. 12, pp. 3209-3217, December 2018, doi: 10.1587/transinf.2018EDP7180.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7180/_p

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@ARTICLE{e101-d_12_3209,
author={Qin DAI, Naoya INOUE, Paul REISERT, Kentaro INUI, },
journal={IEICE TRANSACTIONS on Information},
title={Leveraging Unannotated Texts for Scientific Relation Extraction},
year={2018},
volume={E101-D},
number={12},
pages={3209-3217},
abstract={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.},
keywords={},
doi={10.1587/transinf.2018EDP7180},
ISSN={1745-1361},
month={December},}

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TY - JOUR
TI - Leveraging Unannotated Texts for Scientific Relation Extraction
T2 - IEICE TRANSACTIONS on Information
SP - 3209
EP - 3217
AU - Qin DAI
AU - Naoya INOUE
AU - Paul REISERT
AU - Kentaro INUI
PY - 2018
DO - 10.1587/transinf.2018EDP7180
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2018
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.
ER -