YNU-junyi in BioNLP-OST 2019: Using CNN-LSTM Model with Embeddings for SeeDev Binary Event Extraction

Junyi Li, Xiaobing Zhou, Yuhang Wu, Bin Wang


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.
Anthology ID:
D19-5717
Volume:
Proceedings of The 5th Workshop on BioNLP Open Shared Tasks
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
BioNLP | EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
110–114
Language:
URL:
https://www.aclweb.org/anthology/D19-5717
DOI:
10.18653/v1/D19-5717
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-5717.pdf