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  "Description": "Biterm Topic Models find topics in collections of short\ntexts. It is a word co-occurrence based topic model that learns\ntopics by modeling word-word co-occurrences patterns which are\ncalled biterms. This in contrast to traditional topic models\nlike Latent Dirichlet Allocation and Probabilistic Latent\nSemantic Analysis which are word-document co-occurrence topic\nmodels. A biterm consists of two words co-occurring in the same\nshort text window. This context window can for example be a\ntwitter message, a short answer on a survey, a sentence of a\ntext or a document identifier. The techniques are explained in\ndetail in the paper 'A Biterm Topic Model For Short Text' by\nXiaohui Yan, Jiafeng Guo, Yanyan Lan, Xueqi Cheng (2013)\n<https://github.com/xiaohuiyan/xiaohuiyan.github.io/blob/master/paper/BTM-WWW13.pdf>.",
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