A multiple distributed representation method based on neural network for biomedical event extraction

Background Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification problems. However, traditional state-of-the-art me...
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Autor*in:	Wang, Anran [verfasserIn] Wang, Jian Lin, Hongfei Zhang, Jianhai Yang, Zhihao Xu, Kan

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Biomedical event extraction Distributed representation Deep learning Convolutional neural network

Anmerkung:	© The Author(s). 2017

Übergeordnetes Werk:	Enthalten in: BMC medical informatics and decision making - London : BioMed Central, 2001, 17(2017), Suppl 3 vom: 20. Dez.
Übergeordnetes Werk:	volume:17 ; year:2017 ; number:Suppl 3 ; day:20 ; month:12

Links:	Volltext

DOI / URN:	10.1186/s12911-017-0563-9

Katalog-ID:	SPR028216644

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520			\|a Background Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification problems. However, traditional state-of-the-art methods are based on support vector machine (SVM) with massive manually designed one-hot represented features, which require enormous work but lack semantic relation among words. Methods In this paper, we propose a multiple distributed representation method for biomedical event extraction. The method combines context consisting of dependency-based word embedding, and task-based features represented in a distributed way as the input of deep learning models to train deep learning models. Finally, we used softmax classifier to label the example candidates. Results The experimental results on Multi-Level Event Extraction (MLEE) corpus show higher F-scores of 77.97% in trigger identification and 58.31% in overall compared to the state-of-the-art SVM method. Conclusions Our distributed representation method for biomedical event extraction avoids the problems of semantic gap and dimension disaster from traditional one-hot representation methods. The promising results demonstrate that our proposed method is effective for biomedical event extraction.
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allfields	10.1186/s12911-017-0563-9 doi (DE-627)SPR028216644 (SPR)s12911-017-0563-9-e DE-627 ger DE-627 rakwb eng Wang, Anran verfasserin aut A multiple distributed representation method based on neural network for biomedical event extraction 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2017 Background Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification problems. However, traditional state-of-the-art methods are based on support vector machine (SVM) with massive manually designed one-hot represented features, which require enormous work but lack semantic relation among words. Methods In this paper, we propose a multiple distributed representation method for biomedical event extraction. The method combines context consisting of dependency-based word embedding, and task-based features represented in a distributed way as the input of deep learning models to train deep learning models. Finally, we used softmax classifier to label the example candidates. Results The experimental results on Multi-Level Event Extraction (MLEE) corpus show higher F-scores of 77.97% in trigger identification and 58.31% in overall compared to the state-of-the-art SVM method. Conclusions Our distributed representation method for biomedical event extraction avoids the problems of semantic gap and dimension disaster from traditional one-hot representation methods. The promising results demonstrate that our proposed method is effective for biomedical event extraction. Biomedical event extraction (dpeaa)DE-He213 Distributed representation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Wang, Jian aut Lin, Hongfei aut Zhang, Jianhai aut Yang, Zhihao aut Xu, Kan aut Enthalten in BMC medical informatics and decision making London : BioMed Central, 2001 17(2017), Suppl 3 vom: 20. Dez. (DE-627)328977306 (DE-600)2046490-3 1472-6947 nnns volume:17 year:2017 number:Suppl 3 day:20 month:12 https://dx.doi.org/10.1186/s12911-017-0563-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 Suppl 3 20 12
spelling	10.1186/s12911-017-0563-9 doi (DE-627)SPR028216644 (SPR)s12911-017-0563-9-e DE-627 ger DE-627 rakwb eng Wang, Anran verfasserin aut A multiple distributed representation method based on neural network for biomedical event extraction 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2017 Background Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification problems. However, traditional state-of-the-art methods are based on support vector machine (SVM) with massive manually designed one-hot represented features, which require enormous work but lack semantic relation among words. Methods In this paper, we propose a multiple distributed representation method for biomedical event extraction. The method combines context consisting of dependency-based word embedding, and task-based features represented in a distributed way as the input of deep learning models to train deep learning models. Finally, we used softmax classifier to label the example candidates. Results The experimental results on Multi-Level Event Extraction (MLEE) corpus show higher F-scores of 77.97% in trigger identification and 58.31% in overall compared to the state-of-the-art SVM method. Conclusions Our distributed representation method for biomedical event extraction avoids the problems of semantic gap and dimension disaster from traditional one-hot representation methods. The promising results demonstrate that our proposed method is effective for biomedical event extraction. Biomedical event extraction (dpeaa)DE-He213 Distributed representation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Wang, Jian aut Lin, Hongfei aut Zhang, Jianhai aut Yang, Zhihao aut Xu, Kan aut Enthalten in BMC medical informatics and decision making London : BioMed Central, 2001 17(2017), Suppl 3 vom: 20. Dez. (DE-627)328977306 (DE-600)2046490-3 1472-6947 nnns volume:17 year:2017 number:Suppl 3 day:20 month:12 https://dx.doi.org/10.1186/s12911-017-0563-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 Suppl 3 20 12
allfields_unstemmed	10.1186/s12911-017-0563-9 doi (DE-627)SPR028216644 (SPR)s12911-017-0563-9-e DE-627 ger DE-627 rakwb eng Wang, Anran verfasserin aut A multiple distributed representation method based on neural network for biomedical event extraction 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2017 Background Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification problems. However, traditional state-of-the-art methods are based on support vector machine (SVM) with massive manually designed one-hot represented features, which require enormous work but lack semantic relation among words. Methods In this paper, we propose a multiple distributed representation method for biomedical event extraction. The method combines context consisting of dependency-based word embedding, and task-based features represented in a distributed way as the input of deep learning models to train deep learning models. Finally, we used softmax classifier to label the example candidates. Results The experimental results on Multi-Level Event Extraction (MLEE) corpus show higher F-scores of 77.97% in trigger identification and 58.31% in overall compared to the state-of-the-art SVM method. Conclusions Our distributed representation method for biomedical event extraction avoids the problems of semantic gap and dimension disaster from traditional one-hot representation methods. The promising results demonstrate that our proposed method is effective for biomedical event extraction. Biomedical event extraction (dpeaa)DE-He213 Distributed representation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Wang, Jian aut Lin, Hongfei aut Zhang, Jianhai aut Yang, Zhihao aut Xu, Kan aut Enthalten in BMC medical informatics and decision making London : BioMed Central, 2001 17(2017), Suppl 3 vom: 20. Dez. (DE-627)328977306 (DE-600)2046490-3 1472-6947 nnns volume:17 year:2017 number:Suppl 3 day:20 month:12 https://dx.doi.org/10.1186/s12911-017-0563-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 Suppl 3 20 12
allfieldsGer	10.1186/s12911-017-0563-9 doi (DE-627)SPR028216644 (SPR)s12911-017-0563-9-e DE-627 ger DE-627 rakwb eng Wang, Anran verfasserin aut A multiple distributed representation method based on neural network for biomedical event extraction 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2017 Background Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification problems. However, traditional state-of-the-art methods are based on support vector machine (SVM) with massive manually designed one-hot represented features, which require enormous work but lack semantic relation among words. Methods In this paper, we propose a multiple distributed representation method for biomedical event extraction. The method combines context consisting of dependency-based word embedding, and task-based features represented in a distributed way as the input of deep learning models to train deep learning models. Finally, we used softmax classifier to label the example candidates. Results The experimental results on Multi-Level Event Extraction (MLEE) corpus show higher F-scores of 77.97% in trigger identification and 58.31% in overall compared to the state-of-the-art SVM method. Conclusions Our distributed representation method for biomedical event extraction avoids the problems of semantic gap and dimension disaster from traditional one-hot representation methods. The promising results demonstrate that our proposed method is effective for biomedical event extraction. Biomedical event extraction (dpeaa)DE-He213 Distributed representation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Wang, Jian aut Lin, Hongfei aut Zhang, Jianhai aut Yang, Zhihao aut Xu, Kan aut Enthalten in BMC medical informatics and decision making London : BioMed Central, 2001 17(2017), Suppl 3 vom: 20. Dez. (DE-627)328977306 (DE-600)2046490-3 1472-6947 nnns volume:17 year:2017 number:Suppl 3 day:20 month:12 https://dx.doi.org/10.1186/s12911-017-0563-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 Suppl 3 20 12
allfieldsSound	10.1186/s12911-017-0563-9 doi (DE-627)SPR028216644 (SPR)s12911-017-0563-9-e DE-627 ger DE-627 rakwb eng Wang, Anran verfasserin aut A multiple distributed representation method based on neural network for biomedical event extraction 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2017 Background Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification problems. However, traditional state-of-the-art methods are based on support vector machine (SVM) with massive manually designed one-hot represented features, which require enormous work but lack semantic relation among words. Methods In this paper, we propose a multiple distributed representation method for biomedical event extraction. The method combines context consisting of dependency-based word embedding, and task-based features represented in a distributed way as the input of deep learning models to train deep learning models. Finally, we used softmax classifier to label the example candidates. Results The experimental results on Multi-Level Event Extraction (MLEE) corpus show higher F-scores of 77.97% in trigger identification and 58.31% in overall compared to the state-of-the-art SVM method. Conclusions Our distributed representation method for biomedical event extraction avoids the problems of semantic gap and dimension disaster from traditional one-hot representation methods. The promising results demonstrate that our proposed method is effective for biomedical event extraction. Biomedical event extraction (dpeaa)DE-He213 Distributed representation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Wang, Jian aut Lin, Hongfei aut Zhang, Jianhai aut Yang, Zhihao aut Xu, Kan aut Enthalten in BMC medical informatics and decision making London : BioMed Central, 2001 17(2017), Suppl 3 vom: 20. Dez. (DE-627)328977306 (DE-600)2046490-3 1472-6947 nnns volume:17 year:2017 number:Suppl 3 day:20 month:12 https://dx.doi.org/10.1186/s12911-017-0563-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 Suppl 3 20 12
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authorswithroles_txt_mv	Wang, Anran @@aut@@ Wang, Jian @@aut@@ Lin, Hongfei @@aut@@ Zhang, Jianhai @@aut@@ Yang, Zhihao @@aut@@ Xu, Kan @@aut@@
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author	Wang, Anran
spellingShingle	Wang, Anran misc Biomedical event extraction misc Distributed representation misc Deep learning misc Convolutional neural network A multiple distributed representation method based on neural network for biomedical event extraction
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topic_title	A multiple distributed representation method based on neural network for biomedical event extraction Biomedical event extraction (dpeaa)DE-He213 Distributed representation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213
topic	misc Biomedical event extraction misc Distributed representation misc Deep learning misc Convolutional neural network
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title	A multiple distributed representation method based on neural network for biomedical event extraction
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title_full	A multiple distributed representation method based on neural network for biomedical event extraction
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author_browse	Wang, Anran Wang, Jian Lin, Hongfei Zhang, Jianhai Yang, Zhihao Xu, Kan
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doi_str_mv	10.1186/s12911-017-0563-9
title_sort	multiple distributed representation method based on neural network for biomedical event extraction
title_auth	A multiple distributed representation method based on neural network for biomedical event extraction
abstract	Background Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification problems. However, traditional state-of-the-art methods are based on support vector machine (SVM) with massive manually designed one-hot represented features, which require enormous work but lack semantic relation among words. Methods In this paper, we propose a multiple distributed representation method for biomedical event extraction. The method combines context consisting of dependency-based word embedding, and task-based features represented in a distributed way as the input of deep learning models to train deep learning models. Finally, we used softmax classifier to label the example candidates. Results The experimental results on Multi-Level Event Extraction (MLEE) corpus show higher F-scores of 77.97% in trigger identification and 58.31% in overall compared to the state-of-the-art SVM method. Conclusions Our distributed representation method for biomedical event extraction avoids the problems of semantic gap and dimension disaster from traditional one-hot representation methods. The promising results demonstrate that our proposed method is effective for biomedical event extraction. © The Author(s). 2017
abstractGer	Background Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification problems. However, traditional state-of-the-art methods are based on support vector machine (SVM) with massive manually designed one-hot represented features, which require enormous work but lack semantic relation among words. Methods In this paper, we propose a multiple distributed representation method for biomedical event extraction. The method combines context consisting of dependency-based word embedding, and task-based features represented in a distributed way as the input of deep learning models to train deep learning models. Finally, we used softmax classifier to label the example candidates. Results The experimental results on Multi-Level Event Extraction (MLEE) corpus show higher F-scores of 77.97% in trigger identification and 58.31% in overall compared to the state-of-the-art SVM method. Conclusions Our distributed representation method for biomedical event extraction avoids the problems of semantic gap and dimension disaster from traditional one-hot representation methods. The promising results demonstrate that our proposed method is effective for biomedical event extraction. © The Author(s). 2017
abstract_unstemmed	Background Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification problems. However, traditional state-of-the-art methods are based on support vector machine (SVM) with massive manually designed one-hot represented features, which require enormous work but lack semantic relation among words. Methods In this paper, we propose a multiple distributed representation method for biomedical event extraction. The method combines context consisting of dependency-based word embedding, and task-based features represented in a distributed way as the input of deep learning models to train deep learning models. Finally, we used softmax classifier to label the example candidates. Results The experimental results on Multi-Level Event Extraction (MLEE) corpus show higher F-scores of 77.97% in trigger identification and 58.31% in overall compared to the state-of-the-art SVM method. Conclusions Our distributed representation method for biomedical event extraction avoids the problems of semantic gap and dimension disaster from traditional one-hot representation methods. The promising results demonstrate that our proposed method is effective for biomedical event extraction. © The Author(s). 2017
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title_short	A multiple distributed representation method based on neural network for biomedical event extraction
url	https://dx.doi.org/10.1186/s12911-017-0563-9
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author2	Wang, Jian Lin, Hongfei Zhang, Jianhai Yang, Zhihao Xu, Kan
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up_date	2024-07-03T18:01:44.091Z
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The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification problems. However, traditional state-of-the-art methods are based on support vector machine (SVM) with massive manually designed one-hot represented features, which require enormous work but lack semantic relation among words. Methods In this paper, we propose a multiple distributed representation method for biomedical event extraction. The method combines context consisting of dependency-based word embedding, and task-based features represented in a distributed way as the input of deep learning models to train deep learning models. Finally, we used softmax classifier to label the example candidates. Results The experimental results on Multi-Level Event Extraction (MLEE) corpus show higher F-scores of 77.97% in trigger identification and 58.31% in overall compared to the state-of-the-art SVM method. Conclusions Our distributed representation method for biomedical event extraction avoids the problems of semantic gap and dimension disaster from traditional one-hot representation methods. 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A multiple distributed representation method based on neural network for biomedical event extraction

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