面向细粒度隐式篇章关系识别的远距离监督特征学习算法

Feature Learning by Distant Supervision for Fine-Grained Implicit Discourse Relation Identification

针对中文细粒度隐式篇章关系识别进行研究。考虑细粒度篇章关系的方向性特点,提出一种基于远距离监督的特征学习算法。该算法使用远距离监督的方法,自动标注显式篇章数据,然后利用词与连词之间的相对位置信息,训练各个词的词表达,将词的修辞功能以及关系的方向性编码到密集词表达中,将这样的词表达应用到细粒度隐式篇章关系分类器。实验结果表明,在细粒度隐式篇章关系识别任务中,该方法的分类准确率达到49.79%,比未考虑篇章关系方向性的方法有较大程度的提高。

Aiming at the identification of Chinese fine-grained implicit discourse relation and taking the directionality characteristic in account,the authors propose a feature learning algorithm based on the distant supervision to label explicit discourse data automatically.The relative position information between conjunction and words are applied to train the intensive word representation.Then the rhetorical function of words and the directionality of relations are encoded into the representation of intensive words,which is applied to the relation classification of fine-grained implicit discourses.From the experimental studies of the proposed approach,the classification accuracy reaches 49.79%,which are better than those approaches neglecting the directionality of discourse relations.