基于堆栈降噪自编码的维吾尔语事件共指关系识别

Identification of Uyghur Event Coreference Relationship Based on Stacked Denoising Autoencoder

结合维吾尔语的语言特点,基于堆栈降噪自编码(SDAE),提出一种新的维吾尔语事件共指关系识别方法。将维吾尔语事件两两构成候选事件对,抽取事件基本属性、触发词、事件距离等9项特征,利用Word Embedding富含语义信息的特性,将其计算得到的维吾尔语事件触发词语义相似度作为特征之一,训练SDAE模型,将SDAE的输出作为softmax层的输入,从而分类完成维吾尔语事件共指关系识别任务。实验结果表明,与浅层机器学习模型支持向量机相比,基于深度学习机制的SDAE模型更适用于维吾尔语事件共指关系识别任务,并提升了识别性能。

Based on the characteristics of Uyghur language,a method of identifying Uyghur language event coreference relationship based on Stacked Denoising Autoencoder（SDAE） is proposed.This paper divides the Uyghur events to the candidate event pairs,extracted the nine features,basic characteristics of the event,the trigger word and the event distance.At the same time,the word embedding is used to calculate the semantic similarity of Uyghur events trigger words,taking semantic similarity as one of the features.And then training SDAE model,using softmax to complete the identification task of Uyghur language event coreference relationship.Experimental results show that SDAE is more suitable for the identification task than Support Vector Machine（SVM）,the shallow machine learning model,and the use of word embedding further enhances the identification performance.