A Word Embedding Topic Model for Robust Inference of Topics and Visualization
Sanuj Kumar, Tuan M. V. Le · 2021
Probabilistic topic models for semantic visualization are useful for discovering and visualizing latent topics in document collections. In these models, the inference of topics and visualization is largely based on word co-occurrences within documents. Therefore, when documents in a corpus are short in length, these models may not achieve good results due to the sparsity of word co-occurrences. In this paper, we propose a word embedding topic model (WTM) that is robust to data sparsity when detecting topics and generating visualization of short texts. Extensive experiments conducted on four real-world datasets show that WTM is more effective in dealing with short texts than state-of-the-art models.