@inproceedings{li-etal-2019-ynu,
    title = "{YNU}-junyi in {B}io{NLP}-{OST} 2019: Using {CNN}-{LSTM} Model with Embeddings for {S}ee{D}ev Binary Event Extraction",
    author = "Li, Junyi  and
      Zhou, Xiaobing  and
      Wu, Yuhang  and
      Wang, Bin",
    editor = "Jin-Dong, Kim  and
      Claire, N{\'e}dellec  and
      Robert, Bossy  and
      Louise, Del{\'e}ger",
    booktitle = "Proceedings of the 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/D19-5717/",
    doi = "10.18653/v1/D19-5717",
    pages = "110--114",
    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."
}