面向问答文本的属性分类方法

Attribute Classification for Question-Answer Texts

属性分类是属性级情感分析中的一个重要任务。该任务旨在对文本包含的某些具体属性进行自动分类。已有的属性分类方法研究基本都是面向新闻、评论等文本类型。与已有研究不同的是,该文的研究主要面向问答文本的属性分类任务。针对问答文本的属性分类问题,该文提出了一种多维文本表示的方法。首先,该方法进行中文句子切分;其次,使用LSTM模型对每个子问题和答案学习一个隐层表示;再其次,通过融合多个隐层表示,形成多维文本表示;最后,使用卷积层处理多维文本表示,获得最终分类结果。实验结果表明该方法明显优于传统的属性分类方法。

Attribute classification,as an essential to the task of aspect-based sentiment classification,aims at classifying the category of attribute automatically.In contrast to the existing studies for attribute classification in news and review texts,this paper is focuses on a question-answer(QA)text pair,and a novel approach called multi-dimension textual representation is proposed.Firstly,we segment the question text of a QA text pair into sentences.Then,we leverage LSTM models to encode each sentence in question text and the whole answer text.Finally,we leverage a CNN layer to extract important information in all sentences of question text and the whole answer text.Experiments demonstrate the effectiveness of our proposed approach.