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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
끊임없이 성장하는 과학 문헌에는 엄청난 양의 지식이 존재합니다. 이러한 지식을 효율적으로 파악하기 위해 기계가 과학 문서를 읽고 분석하도록 훈련시키는 다양한 계산 작업이 제안됩니다. 이러한 작업 중 하나인 과학적 관계 추출(Scientific Relation Extraction)은 과학 문서의 개체 간 과학적 의미 관계를 자동으로 캡처하는 것을 목표로 합니다. 일반적으로 Wikipedia와 같이 일반적으로 사용되는 제한된 수의 지식 베이스만이 관계 추출을 위한 배경 지식의 소스로 사용됩니다. 이 연구에서 우리는 주석이 없는 과학 논문이 관계 추출을 위한 외부 배경 정보의 소스로 활용될 수도 있다는 가설을 세웠습니다. 우리의 가설을 바탕으로 주석이 없는 과학 논문에서 배경 정보를 추출할 수 있는 모델을 제안합니다. RANIS 코퍼스[1]에 대한 우리의 실험은 과학 논문에서 관계 추출에 대해 제안된 모델의 효율성을 입증했습니다.
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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부
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
부
@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},}
부
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 -