title: Embedding assisted prediction architecture for event trigger identification.
abstract: Molecular events normally have significant meanings since they describe important biological interactions or alternations such as binding of a protein. As a crucial step of biological event extraction, event trigger identification has attracted much attention and many methods have been proposed. Traditionally those methods can be categorised into rule-based approach and machine learning approach and machine learning-based approaches have demonstrated its potential and outperformed rule-based approaches in many situations. However, machine learning-based approaches still face several challenges among which a notable one is how to model semantic and syntactic information of different words and incorporate it into the prediction model. There exist many ways to model semantic and syntactic information, among which word embedding is an effective one. Therefore, in order to address this challenge, in this study, a word embedding assisted neural network prediction model is proposed to conduct event trigger identification. The experimental study on commonly used dataset has shown its potential. It is believed that this study could offer researchers insights into semantic-aware solutions for event trigger identification.
authors: Sino-French Engineer School, Beihang University, Beijing 100191, P. R. China.
authors: Yifan
authors: Y
authors: Nie
authors: Wenge
authors: W
authors: Rong
authors: Yiyuan
authors: Y
authors: Zhang
authors: Yuanxin
authors: Y
authors: Ouyang
authors: Zhang
authors: Z
authors: Xiong
mesh_terms: D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D009323:Natural Language Processing; D016571:Neural Networks, Computer; D012660:Semantics
keywords: Neural networks; event trigger identification; skip-gram language model; word embedding