Bag of bitems为短文本建模
Bag of biterms modeling for short texts
摘要：
社交媒体文本具有简短、快速、动态等特点，分析这些文本面临着许多挑战。短文本不能提供足够的上下文信息，导致传统统计模型失效。此外，许多应用程序往往面临大量的和动态的短文本，造成各种计算挑战，目前的批处理学习算法。本文提出了一种新的框架，即BagofBitemsModeling（BBM），用于建模大规模的，动态的，简短的文本集合。BBM包括两个主要成分：（1）表示文档的Bag of Bitems（BoB）概念，以及（2）帮助统计模型包含BoB的简单方法。我们的框架可以很容易地部署为一个大类的概率模型，我们证明了它的有用性与两个著名的模型：潜在的狄利克雷分配（LDA）和分层狄利克雷过程（HDP）。BBM的主要优点是：（1）通过Bag of Bitems强调词的内涵和共现，增加了文档的长度，使上下文更加连贯;（2）继承了原语的推理和学习算法，使短文本的在线和流算法设计变得简单。大量的实验表明，BBM优于几个国家的最先进的模型。我们还指出，BoB表示性能优于传统的表示（例如，袋的话，tf-idf），即使是正常的文本。
英文摘要：
Analyzing texts from social media encounters many challenges due to their unique characteristics of shortness, massiveness, and dynamic. Short texts do not provide enough context information, causing the failure of the traditional statistical models. Furthermore, many applications often face with massive and dynamic short texts, causing various computational challenges to the current batch learning algorithms. This paper presents a novel framework, namely Bag of Biterms Modeling (BBM), for modeling massive, dynamic, and short text collections. BBM comprises of two main ingredients: (1) the concept of Bag of Biterms (BoB) for representing documents, and (2) a simple way to help statistical models to include BoB. Our framework can be easily deployed for a large class of probabilistic models, and we demonstrate its usefulness with two well-known models: Latent Dirichlet Allocation (LDA) and Hierarchical Dirichlet Process (HDP). By exploiting both terms (words) and biterms (pairs of words), the major advantages of BBM are: (1) it enhances the length of the documents and makes the context more coherent by emphasizing the word connotation and co-occurrence via Bag of Biterms, (2) it inherits inference and learning algorithms from the primitive to make it straightforward to design online and streaming algorithms for short texts. Extensive experiments suggest that BBM outperforms several state-of-the-art models. We also point out that the BoB representation performs better than the traditional representations (e.g, Bag of Words, tf-idf) even for normal texts.
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。
