DUTIR in BioNLP-ST 2016: Utilizing Convolutional Network and Distributed Representation to Extract Complicate Relations

By Honglei Li, Jianhai Zhang, Jian Wang, Hongfei Lin, Zhihao Yang. 2016

12 citations recorded by the source corpus.

We participate in the two event extraction tasks of BioNLP 2016 Shared Task: binary relation extraction of SeeDev task and localization relations extraction of Bacteria Biotope task. Convolutional neural network (CNN) is employed to model the sentences by convolution and max-pooling operation from raw input with word embedding. Then, full connected neural network is used to learn senior and significant features automatically. The proposed model mainly contains two modules: distributive semantic representation building, such as word embedding, POS embedding, distance embedding and entity type embedding, and CNN model training. The results with F-score of 0.370 and 0.478 in our participant tasks, which were evaluated on the test data set, show that our proposed method contributes to binary relation extraction effectively and can reduce the impact of artificial feature engineering through automatically feature learning.

Subfields: Molecular Biology

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In Molecular Biology

1. Donghong Ji
2. Guohong Fu
3. Fei Li
4. Meishan Zhang
5. Adrian Ulges
6. Markus Eberts

Citations in the corpus

• A neural joint model for entity and relation extraction from biomedical text (2017, background): “Li et al. [27] and Mehryary et al. [28] used similar approaches with [24, 25], but they only focused on relation classification with given entities.”