VSEC-LDA: Boosting Topic Modeling with Embedded Vocabulary Selection

• URL: http://arxiv.org/abs/2001.05578v1
• Date: Wed, 15 Jan 2020 22:16:24 GMT
• Title: VSEC-LDA: Boosting Topic Modeling with Embedded Vocabulary Selection
• Authors: Yuzhen Ding, Baoxin Li
• Abstract summary: We propose a new approach to topic modeling, termed Vocabulary-Selection-Embedded Correspondence-LDA (VSEC-LDA) VSEC-LDA learns the latent model while simultaneously selecting most relevant words. The selection of words is driven by an entropy-based metric that measures the relative contribution of the words to the underlying model.
• Score: 20.921010767231923
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Topic modeling has found wide application in many problems where latent structures of the data are crucial for typical inference tasks. When applying a topic model, a relatively standard pre-processing step is to first build a vocabulary of frequent words. Such a general pre-processing step is often independent of the topic modeling stage, and thus there is no guarantee that the pre-generated vocabulary can support the inference of some optimal (or even meaningful) topic models appropriate for a given task, especially for computer vision applications involving "visual words". In this paper, we propose a new approach to topic modeling, termed Vocabulary-Selection-Embedded Correspondence-LDA (VSEC-LDA), which learns the latent model while simultaneously selecting most relevant words. The selection of words is driven by an entropy-based metric that measures the relative contribution of the words to the underlying model, and is done dynamically while the model is learned. We present three variants of VSEC-LDA and evaluate the proposed approach with experiments on both synthetic and real databases from different applications. The results demonstrate the effectiveness of built-in vocabulary selection and its importance in improving the performance of topic modeling.

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