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A language-independent neural network for event detection

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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.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61632011, 61772156, 61702137).

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Authors and Affiliations

  1. Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Xiaocheng Feng, Bing Qin & Ting Liu

Authors
  1. Xiaocheng Feng
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  2. Bing Qin
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  3. Ting Liu
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Correspondence to Bing Qin.

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Feng, X., Qin, B. & Liu, T. A language-independent neural network for event detection. Sci. China Inf. Sci. 61, 092106 (2018). https://doi.org/10.1007/s11432-017-9359-x

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  • DOI: https://doi.org/10.1007/s11432-017-9359-x

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