A dirichlet multinomial mixture model-based approach for short text clustering

By Jianhua Yin, Jianyong Wang. Knowledge Discovery and Data Mining, 2014

581 citations recorded by the source corpus.

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Topic Modeling for Short Texts with Auxiliary Word Embeddings (2016, methodology): “Dirichlet Mixture Model is a generative probabilistic model with the assumption that a document is generated from a single topic [26,40].”
Improving Topic Models with Latent Feature Word Representations (2015, methodology): “…for the document, and then the topic-to-word Dirichlet multinomial component generates all the words in the document from the same selected topic: Yin and Wang (2014) introduced a collapsed Gibbs sampling algorithm for the DMM model in which a topic z d is sampled for the document d us-ing the…”
Short Text Topic Modeling Techniques, Applications, and Performance: A Survey (2019, methodology): “Yin et al. [18] proposed a collapsed Gibbs sampling algorithm for DMM.”
Self-Taught Convolutional Neural Networks for Short Text Clustering (2017, background): “example, Yin and Wang [6] proposed a Dirichlet multinomial mixture modelbased approach for short text clustering.”
Election Campaigning on Social Media: Politicians, Audiences, and the Mediation of Political Communication on Facebook and Twitter (2018, background): “This assumption handles short documents such as social media messages or survey responses well (Yin & Wang, 2014).”
A Survey on Text Classification: From Traditional to Deep Learning (2020, unknown): “The SICK contains almost 10,000 English sentence pairs.”