一种基于句子嵌入的微博话题检测方法
A novel sentence embedding based topic detection method for micro-blog
摘要：
主题检测是一项具有挑战性的任务，特别是在不知道主题的确切数量的情况下。提出了一种基于神经网络的微博话题检测方法。我们使用一个无监督的神经句子嵌入模型将博客映射到一个嵌入空间。我们的模型是一个加权幂均值词嵌入模型，权重由注意力机制计算。实验结果表明，我们的嵌入方法在句子聚类中的性能优于基线。此外，我们提出了一种改进的聚类算法称为关系感知DBSCAN（RADBSCAN）。它可以从微博数据集中发现主题，主题数量取决于数据集本身的性质。此外，为了解决参数敏感的问题，我们采取博客转发关系作为两个独立的集群之间的桥梁。最后，我们在新浪微博的数据集上验证了我们的方法。实验结果表明，该方法能够成功地检测出所有的主题，并提取出每个主题中的关键词。
英文摘要：
Topic detection is a challenging task, especially without knowing the exact number of topics. In this paper, we present a novel approach based on neural network to detect topics in the micro-blogging dataset. We use an unsupervised neural sentence embedding model to map the blogs to an embedding space. Our model is a weighted power mean word embedding model, and the weights are calculated by attention mechanism. Experimental result shows our embedding method performs better than baselines in sentence clustering. In addition, we propose an improved clustering algorithm referred as relationship-aware DBSCAN (RADBSCAN). It can discover topics from a micro-blogging dataset, and the topic number depends on dataset character itself. Moreover, in order to solve the problem of parameters sensitive, we take blog forwarding relationship as a bridge of two independent clusters. Finally, we validate our approach on a dataset from sina micro-blog. The result shows that we can detect all the topics successfully and extract keywords in each topic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
