Abstract
Trigger detection as the preceding task is of great importance in biomedical event extraction. By now, most of the state-of-the-art systems have been based on single classifiers, and the words encoded by one-hot are unable to represent the semantic information. In this paper, we utilize hybrid methods integrating word embeddings to get higher performance. In hybrid methods, first, multiple single classifiers are constructed based on rich manual features including dependency and syntactic parsed results. Then multiple predicting results are integrated by set operation, voting and stacking method. Hybrid methods can take advantage of the difference among classifiers and make up for their deficiencies and thus improve performance. Word embeddings are learnt from large scale unlabeled texts and integrated as unsupervised features into other rich features based on dependency parse graphs, and thus a lot of semantic information can be represented. Experimental results show our method outperforms the state-of-the-art systems.
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The authors gratefully acknowledge the financial support provided by the National Natural Science Foundation of China under No. 61672126, 61173101.
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Li, L., Qin, M., Huang, D. (2016). Biomedical Event Trigger Detection Based on Hybrid Methods Integrating Word Embeddings. In: Chen, H., Ji, H., Sun, L., Wang, H., Qian, T., Ruan, T. (eds) Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data. CCKS 2016. Communications in Computer and Information Science, vol 650. Springer, Singapore. https://doi.org/10.1007/978-981-10-3168-7_7
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