Pairwise Topic Model via relation extraction
Xiaoli Song, Yue Shang, Ling Yuan, Mengwen Liu, Xiaohua Hu · 2014
Topic modeling is a powerful tool to model documents to find their underlying topics. However, the unstructured nature of the raw text makes it hard to model the semantic relationship between the text units, which may be the words, phrases or sentences, and thus even harder to model their corresponding underlying topics. In our work, we try to examine the pairwise relationship of the underlying topics through relation extraction. We first extract the entity pairs within one relation tuple out of the raw text. Then, we model the relationship between the entity pairs by adding the dependencies between entities and their corresponding topics. We propose six different versions of Pairwise Topic Model (PTM) to simultaneously discover the latent topics and their pairwise relationship. The experiment on four data sets (AP news articles, DUC 2004 task2, Clinical Notes and Neuroscience Papers) shows the PTM models are better-structured language model than the traditional topic model Latent Dirichlet Allocation (LDA). Also, empirical results show that the proposed Pairwise Topic Models (PTMs) can explicitly explain how two topics are related.