The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Une quantité considérable de connaissances est présente dans la littérature scientifique en constante augmentation. Afin d’appréhender efficacement ces connaissances, diverses tâches informatiques sont proposées pour entraîner les machines à lire et analyser des documents scientifiques. L'une de ces tâches, Scientific Relation Extraction, vise à capturer automatiquement les relations sémantiques scientifiques entre les entités dans les documents scientifiques. Conventionnellement, seul un nombre limité de bases de connaissances couramment utilisées, telles que Wikipédia, sont utilisées comme source de connaissances de base pour l'extraction de relations. Dans ce travail, nous émettons l’hypothèse que des articles scientifiques non annotés pourraient également être utilisés comme source d’informations externes pour l’extraction de relations. Sur la base de notre hypothèse, nous proposons un modèle capable d'extraire des informations de base à partir d'articles scientifiques non annotés. Nos expériences sur le corpus RANIS [1] prouvent l'efficacité du modèle proposé sur l'extraction de relations à partir d'articles scientifiques.
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
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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},}
Copier
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 -