论文标题
短文本的伽玛 - 波森混合主题模型
A Gamma-Poisson Mixture Topic Model for Short Text
论文作者
论文摘要
大多数主题模型都是在文档遵循多项式分布的假设下构建的。泊松分布是描述计数数据概率的替代分布。对于主题建模，泊松分布描述了固定长度文档中单词的出现数量。泊松分布已成功地应用于文本分类中，但其在主题建模中的应用尚未得到充分记录，特别是在生成概率模型的背景下。此外，文献中的少数泊松主题模型是混合模型，假设文档是由主题混合产生的。在这项研究中，我们专注于短文。许多研究表明，混合模型的更简单假设更适合短文本。使用混合模型，与混合模型相反，生成的假设是文档是由单个主题生成的。一个主题模型，这是一个单一文档的假设，是Dirichlet-Multinoilial混合模型。这项工作的主要贡献是一种新的Gamma-Poisson混合物模型，以及该模型崩溃的Gibbs采样器。倒塌的Gibbs采样器推导的好处是，该模型能够自动选择语料库中包含的主题数量。结果表明，在选择标记的Corpora中的主题数量时，Gamma-Poisson混合物模型的性能优于Dirichlet-Multinoilial混合模型。此外，与Dirichlet-Multinomial混合模型相比，Gamma-Poisson混合物产生的主题连贯得分更好，因此使其成为简短文本主题建模挑战性任务的可行选择。
Most topic models are constructed under the assumption that documents follow a multinomial distribution. The Poisson distribution is an alternative distribution to describe the probability of count data. For topic modelling, the Poisson distribution describes the number of occurrences of a word in documents of fixed length. The Poisson distribution has been successfully applied in text classification, but its application to topic modelling is not well documented, specifically in the context of a generative probabilistic model. Furthermore, the few Poisson topic models in literature are admixture models, making the assumption that a document is generated from a mixture of topics. In this study, we focus on short text. Many studies have shown that the simpler assumption of a mixture model fits short text better. With mixture models, as opposed to admixture models, the generative assumption is that a document is generated from a single topic. One topic model, which makes this one-topic-per-document assumption, is the Dirichlet-multinomial mixture model. The main contributions of this work are a new Gamma-Poisson mixture model, as well as a collapsed Gibbs sampler for the model. The benefit of the collapsed Gibbs sampler derivation is that the model is able to automatically select the number of topics contained in the corpus. The results show that the Gamma-Poisson mixture model performs better than the Dirichlet-multinomial mixture model at selecting the number of topics in labelled corpora. Furthermore, the Gamma-Poisson mixture produces better topic coherence scores than the Dirichlet-multinomial mixture model, thus making it a viable option for the challenging task of topic modelling of short text.
