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
생체의학 이벤트 추출은 의학 연구 및 질병 예방에 핵심적인 역할을 하는 정보 추출에서 중요하고 도전적인 작업입니다. 기존의 이벤트 감지 방법은 대부분 도메인 지식과 정교하게 설계된 기능에 의존하는 얕은 기계 학습 방법을 기반으로 합니다. 또 다른 과제는 대부분의 작품이 단어와 문장을 동일하게 다루기 때문에 일부 중요한 정보와 단어 또는 논증 간의 상호 작용이 무시될 수 있다는 것입니다. 따라서 우리는 이벤트 추출을 위해 수작업으로 복잡한 특징 추출을 건너뛸 수 있는 BLSTM(양방향 장단기 기억) 신경망을 사용합니다. 또한, 문장 내 단어의 중요성을 결정하는 단어 수준 주의와 관련 주장의 중요성을 결정하는 문장 수준 주의를 포함하는 다단계 주의 메커니즘을 제안합니다. 마지막으로 종속성 단어 임베딩을 훈련하고 문장 벡터를 추가하여 의미 정보를 풍부하게 합니다. 실험 결과에 따르면 우리 모델은 일반적으로 사용되는 생물 의학 이벤트 추출 데이터 세트(MLEE)에서 59.61%의 F 점수를 달성했으며 이는 다른 최첨단 방법보다 성능이 뛰어납니다.
Xinyu HE
Dalian University of Technology
Lishuang LI
Dalian University of Technology
Xingchen SONG
Dalian University of Technology
Degen HUANG
Dalian University of Technology
Fuji REN
University of Tokushima
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부
Xinyu HE, Lishuang LI, Xingchen SONG, Degen HUANG, Fuji REN, "Multi-Level Attention Based BLSTM Neural Network for Biomedical Event Extraction" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 9, pp. 1842-1850, September 2019, doi: 10.1587/transinf.2018EDP7268.
Abstract: Biomedical event extraction is an important and challenging task in Information Extraction, which plays a key role for medicine research and disease prevention. Most of the existing event detection methods are based on shallow machine learning methods which mainly rely on domain knowledge and elaborately designed features. Another challenge is that some crucial information as well as the interactions among words or arguments may be ignored since most works treat words and sentences equally. Therefore, we employ a Bidirectional Long Short Term Memory (BLSTM) neural network for event extraction, which can skip handcrafted complex feature extraction. Furthermore, we propose a multi-level attention mechanism, including word level attention which determines the importance of words in a sentence, and the sentence level attention which determines the importance of relevant arguments. Finally, we train dependency word embeddings and add sentence vectors to enrich semantic information. The experimental results show that our model achieves an F-score of 59.61% on the commonly used dataset (MLEE) of biomedical event extraction, which outperforms other state-of-the-art methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7268/_p
부
@ARTICLE{e102-d_9_1842,
author={Xinyu HE, Lishuang LI, Xingchen SONG, Degen HUANG, Fuji REN, },
journal={IEICE TRANSACTIONS on Information},
title={Multi-Level Attention Based BLSTM Neural Network for Biomedical Event Extraction},
year={2019},
volume={E102-D},
number={9},
pages={1842-1850},
abstract={Biomedical event extraction is an important and challenging task in Information Extraction, which plays a key role for medicine research and disease prevention. Most of the existing event detection methods are based on shallow machine learning methods which mainly rely on domain knowledge and elaborately designed features. Another challenge is that some crucial information as well as the interactions among words or arguments may be ignored since most works treat words and sentences equally. Therefore, we employ a Bidirectional Long Short Term Memory (BLSTM) neural network for event extraction, which can skip handcrafted complex feature extraction. Furthermore, we propose a multi-level attention mechanism, including word level attention which determines the importance of words in a sentence, and the sentence level attention which determines the importance of relevant arguments. Finally, we train dependency word embeddings and add sentence vectors to enrich semantic information. The experimental results show that our model achieves an F-score of 59.61% on the commonly used dataset (MLEE) of biomedical event extraction, which outperforms other state-of-the-art methods.},
keywords={},
doi={10.1587/transinf.2018EDP7268},
ISSN={1745-1361},
month={September},}
부
TY - JOUR
TI - Multi-Level Attention Based BLSTM Neural Network for Biomedical Event Extraction
T2 - IEICE TRANSACTIONS on Information
SP - 1842
EP - 1850
AU - Xinyu HE
AU - Lishuang LI
AU - Xingchen SONG
AU - Degen HUANG
AU - Fuji REN
PY - 2019
DO - 10.1587/transinf.2018EDP7268
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2019
AB - Biomedical event extraction is an important and challenging task in Information Extraction, which plays a key role for medicine research and disease prevention. Most of the existing event detection methods are based on shallow machine learning methods which mainly rely on domain knowledge and elaborately designed features. Another challenge is that some crucial information as well as the interactions among words or arguments may be ignored since most works treat words and sentences equally. Therefore, we employ a Bidirectional Long Short Term Memory (BLSTM) neural network for event extraction, which can skip handcrafted complex feature extraction. Furthermore, we propose a multi-level attention mechanism, including word level attention which determines the importance of words in a sentence, and the sentence level attention which determines the importance of relevant arguments. Finally, we train dependency word embeddings and add sentence vectors to enrich semantic information. The experimental results show that our model achieves an F-score of 59.61% on the commonly used dataset (MLEE) of biomedical event extraction, which outperforms other state-of-the-art methods.
ER -