A language-independent neural network for event detection
Science China Information SciencesPublished 3 August 2018
Xiaocheng Feng, Bing Qin, Ting Liu
Citations135
SJR quartileQ1
SJR score1.48
SNIP2.05
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TL;DR
A language-independent neural network is developed to capture both sequence and chunk information from specific contexts and use them to train an event detector for multiple languages without any manually encoded features.
Abstract
Event detection remains a challenge because of the difficulty of encoding the word semantics in various contexts. Previous approaches have heavily depended on language-specific knowledge and preexisting natural language processing tools. However, not all languages have such resources and tools available compared with English language. A more promising approach is to automatically learn effective features from data, without relying on language-specific resources. In this study, we develop a language-independent neural network to capture both sequence and chunk information from specific contexts and use them to train an event detector for multiple languages without any manually encoded features. Experiments show that our approach can achieve robust, efficient and accurate results for various languages. In the ACE 2005 English event detection task, our approach achieved a 73.4% F-score with an average of 3.0% absolute improvement compared with state-of-the-art. Additionally, our experimental results are competitive for Chinese and Spanish.
Keywords
Computer Science
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