基于混合表示的中文事件检测方法研究

传统中文事件检测方法采用人工定义的特征表示候选触发词,耗时耗力。基于神经网络的特征学习方法在中英文事件检测任务中得到了验证。现有的基于神经网络的中文事件检测方法初步探索了字信息对解决分词错误的作用。字是中文的最小结构单元和语义表示单元。词语的字符级信息能够提供词语的结构性信息和辅助词语级语义。该文研究了字/词混合神经网络特征对于解决中文事件数据集未登录词问题的作用。采用神经网络模型分别学习词语的词语级表示和字符级表示,进而拼接得到词语的混合表示。实验结果表明,基于字/词混合表示的中文神经网络事件检测模型的F₁值比当前最好的模型高2.5%。

Hybrid Representation Based Chinese Event Detection

Neural network based feature learning methods had been proven to be effective in Chinese/English event detection task. This paper further explores character-word-level neural features on solving out-of-vocabulary phenomenon in Chinese event detection. Two neural network models are applied to learn word-level representation and character-level representation, respectively. Hybrid representation for each word is obtained by concatenating word-level and character-level representation. Experimental results show that the proposed hybrid representation-based neural Chinese event detection model outperforms state-of-the-art results by 2.5% on F₁.