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
Uma enorme quantidade de conhecimento está presente na literatura científica cada vez maior. Para apreender esse conhecimento de forma eficiente, são propostas diversas tarefas computacionais que treinam máquinas para ler e analisar documentos científicos. Uma dessas tarefas, Extração de Relações Científicas, visa capturar automaticamente relações semânticas científicas entre entidades em documentos científicos. Convencionalmente, apenas um número limitado de bases de conhecimento comumente utilizadas, como a Wikipédia, são utilizadas como fonte de conhecimento prévio para extração de relações. Neste trabalho, levantamos a hipótese de que artigos científicos não anotados também poderiam ser utilizados como fonte de informações externas para extração de relações. Com base em nossa hipótese, propomos um modelo capaz de extrair informações básicas de artigos científicos não anotados. Nossos experimentos no corpus RANIS [1] comprovam a eficácia do modelo proposto na extração de relações de artigos científicos.
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},}
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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 -