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        <title>YNU-junyi in BioNLP-OST 2019: Using CNN-LSTM Model with Embeddings for SeeDev Binary Event Extraction</title>
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        <namePart type="given">Junyi</namePart>
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        <namePart type="given">Xiaobing</namePart>
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            <title>Proceedings of the 5th Workshop on BioNLP Open Shared Tasks</title>
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            <namePart type="given">Kim</namePart>
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    <abstract>We participated in the BioNLP 2019 Open Shared Tasks: binary relation extraction of SeeDev task. The model was constructed us- ing convolutional neural networks (CNN) and long short term memory networks (LSTM). The full text information and context information were collected using the advantages of CNN and LSTM. The model consisted of two main modules: distributed semantic representation construction, such as word embedding, distance embedding and entity type embed- ding; and CNN-LSTM model. The F1 value of our participated task on the test data set of all types was 0.342. We achieved the second highest in the task. The results showed that our proposed method performed effectively in the binary relation extraction.</abstract>
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%0 Conference Proceedings
%T YNU-junyi in BioNLP-OST 2019: Using CNN-LSTM Model with Embeddings for SeeDev Binary Event Extraction
%A Li, Junyi
%A Zhou, Xiaobing
%A Wu, Yuhang
%A Wang, Bin
%Y Jin-Dong, Kim
%Y Claire, Nédellec
%Y Robert, Bossy
%Y Louise, Deléger
%S Proceedings of the 5th Workshop on BioNLP Open Shared Tasks
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F li-etal-2019-ynu
%X We participated in the BioNLP 2019 Open Shared Tasks: binary relation extraction of SeeDev task. The model was constructed us- ing convolutional neural networks (CNN) and long short term memory networks (LSTM). The full text information and context information were collected using the advantages of CNN and LSTM. The model consisted of two main modules: distributed semantic representation construction, such as word embedding, distance embedding and entity type embed- ding; and CNN-LSTM model. The F1 value of our participated task on the test data set of all types was 0.342. We achieved the second highest in the task. The results showed that our proposed method performed effectively in the binary relation extraction.
%R 10.18653/v1/D19-5717
%U https://aclanthology.org/D19-5717
%U https://doi.org/10.18653/v1/D19-5717
%P 110-114

Markdown (Informal)

[YNU-junyi in BioNLP-OST 2019: Using CNN-LSTM Model with Embeddings for SeeDev Binary Event Extraction](https://aclanthology.org/D19-5717) (Li et al., BioNLP 2019)

• YNU-junyi in BioNLP-OST 2019: Using CNN-LSTM Model with Embeddings for SeeDev Binary Event Extraction (Li et al., BioNLP 2019)

ACL

• Junyi Li, Xiaobing Zhou, Yuhang Wu, and Bin Wang. 2019. YNU-junyi in BioNLP-OST 2019: Using CNN-LSTM Model with Embeddings for SeeDev Binary Event Extraction. In Proceedings of the 5th Workshop on BioNLP Open Shared Tasks, pages 110–114, Hong Kong, China. Association for Computational Linguistics.