Automatically Labelling Sentiment-Bearing Topics with Descriptive Sentence Labels
Automatically Labelling Sentiment-Bearing Topics with Descriptive Sentence Labels
复制标题
DOI：
10.1007/978-3-319-59569-6_38
复制
发表时间：
2017-06
期刊：
影响因子：
--
通讯作者：
M. Barawi;Chenghua Lin;Advaith Siddharthan
中科院分区：
文献类型：
--
作者：
M. Barawi;Chenghua Lin;Advaith Siddharthan
在本文中，我们提出了一个简单而有效的方法，自动标记的情感承载的主题与描述性的句子标签。具体来说，我们的方法包括两个组成部分：（i）一个机制，可以自动学习的相关性，在语料库中的底层句子的情感承载的主题;和（ii）一个句子排名算法的标签选择，共同考虑主题句子的相关性以及方面和情感的共同覆盖。据我们所知，我们是第一个研究给有感情的话题贴标签的问题的。我们的实验结果表明，我们的方法优于四个强大的基线，并证明了我们的句子标签在促进主题理解和解释的有效性。
In this paper, we propose a simple yet effective approach for automatically labelling sentiment-bearing topics with descriptive sentence labels. Specifically, our approach consists of two components: (i) a mechanism which can automatically learn the relevance to sentiment-bearing topics of the underlying sentences in a corpus; and (ii) a sentence ranking algorithm for label selection that jointly considers topic-sentence relevance as well as aspect and sentiment co-coverage. To our knowledge, we are the first to study the problem of labelling sentiment-bearing topics. Our experimental results show that our approach outperforms four strong baselines and demonstrates the effectiveness of our sentence labels in facilitating topic understanding and interpretation.