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Topic modeling is a widely studied foundational and interesting problem in the text mining domains. Conventional topic models based on word co-occurrences infer the hidden semantic structure from a corpus of documents. However, due to the limited length of short text, data sparsity impedes the inference process of conventional topic models and causes unsatisfactory results on short texts. In fact, each short text usually contains a limited number of topics, and understanding semantic content ofdoi:10.1109/access.2020.2973207 fatcat:qrmkhfoxqjb4bcutcyicuwvpiy