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Indexed by:会议论文

Date of Publication:2016-09-19

Included Journals:EI、CPCI-S、SCIE

Volume:650

Page Number:67-79

Key Words:Trigger detection; Word embeddings; Hybrid methods; Rich features

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.