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- W2255612897 abstract "AbstractMost nonparametric topic models such as Hierarchical Dirichlet Processes, when viewed as an infinite-dimensional extension to the Latent Dirichlet Allocation, rely on the bag-of-words assumption. They thus lose the semantic ordering of the words inherent in the text which can give an extra leverage to the computational model. We present a new nonparametric topic model that not only maintains the word order in the topic discovery process, but also generates topical n-gram words leading to more interpretable latent topics in the family of the nonparametric topic models. Our experimental results show an improved performance over the current state-of-the-art topic models in document modeling and generating n-gram words in topics.KeywordsBayesian NonparametricsN-gram wordsPerplexityN-gram topic modelCollocations" @default.
- W2255612897 created "2016-06-24" @default.
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- W2255612897 date "2013-01-01" @default.
- W2255612897 modified "2023-10-14" @default.
- W2255612897 title "A Nonparametric N-Gram Topic Model with Interpretable Latent Topics" @default.
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- W2255612897 doi "https://doi.org/10.1007/978-3-642-45068-6_7" @default.
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