Abstract
Topic modeling is a machine learning technique for discovering semantic topics from a document collection. It typically assumes that a document is a multinomial distribution over latent topics, and a topic is a multinomial distribution over words. By capturing the co-occurrence statistics of words in the documents, it uncovers these distributions which indicate important semantic relationships. Topic modeling has been widely studied in machine learning, text mining, and natural language processing (NLP). This chapter gives an introduction to topic modeling. It covers both the fundamental techniques and some of its important applications in NLP.
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Chen, Z., Liu, B. (2017). Topic Models for NLP Applications. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_906
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_906
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