主题抽取是意见挖掘的核心任务之一。该文面向维吾尔语评论文本, 针对显式主题和隐式主题, 提出了一种陈述级的主题抽取方法。该方法采用GLR-Cascaded LDA模型抽取段落级的局部主题、篇章级的全局主题, 建立全局—局部主题关系, 并将这些关系对应到每个意见陈述中; 然后运用Bootstrapping和模式匹配的方法进行显式陈述的主题抽取; 最后使用隐式主题推断算法推断隐式陈述的主题。主题抽取的最终目标是为每个意见陈述建立意见陈述—主题四元组<OC, GT, LT, CT>。实验结果证明了该方法在主题抽取任务中的有效性。

Topic extraction is one of the core tasks of opinion mining. This paper proposes a claim-level topic extraction method, which aims at extracting explicit topics and implicit topics of Uighur comment texts. This method uses GLR-Cascaded LDA (Cascaded LDA model for global topic, local topic and the relation between them, GLR-Cascaded LDA) to extract the local topics of paragraph level, global topics of document level, establish the global-local topic relationship, and corresponds the relationships to each opinion claim. It adopts Bootstrapping and pattern matching to extract the topics of explicit claims. Finally, the implicit topic inference algorithm is applied to deduce the topics of implicit claims. The ultimate goal of topic extraction is to establish an opinion quadruple of claim-topic <OC, GT, LT, LT> for each opinion claim. Experimental results indicate the effectiveness of the proposed method in topic extraction task.
Key wordstopic extraction; claim level; explicit topic; implicit topic; Uighur