Open AccessExploring Multiple Embedded Features on Event Extraction
Author(s) -
Shi-Xiang Yi,
Chunyan Li
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1267/1/012033
Subject(s) - computer science , event (particle physics) , pipeline (software) , representation (politics) , artificial intelligence , artificial neural network , point (geometry) , natural language processing , machine learning , natural language understanding , feature extraction , natural language , complex event processing , data mining , physics , geometry , mathematics , process (computing) , quantum mechanics , politics , political science , law , programming language , operating system
In recent years, the neural network method can automatically learn effectively features. Unlike traditional discrete features, neural network features are mostly continuous features and can be automatically combined to build higher-level features. The efficiency of the features has been proven in numerous tasks in natural language processing and has led to breakthroughs. In this paper, we propose a event extraction system based on combination of multiple embedded features. Our work is mainly based on the three aspects: (1) traditional pipeline systems have serious error propagation problems; (2) there are several different event descriptions in the text; (3) representation learning can provide rich semantic and syntactic representation. As a result, we achieve competitive performance, specifically, F1-measure of 60.25 in event extraction. Meanwhile, evaluation results point out some shortcomings that need to be addressed in future work.