Short Text Classification Based on Latent Topic Modeling and Word Embedding
نویسندگان
چکیده
منابع مشابه
Topic Modeling and Classification of Cyberspace Papers Using Text Mining
The global cyberspace networks provide individuals with platforms to can interact, exchange ideas, share information, provide social support, conduct business, create artistic media, play games, engage in political discussions, and many more. The term cyberspace has become a conventional means to describe anything associated with the Internet and the diverse Internet culture. In fact, cyberspac...
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Topic modeling and word embedding are two important techniques for deriving latent semantics from data. General-purpose topic models typically work in coarse granularity by capturing word co-occurrence at the document/sentence level. In contrast, word embedding models usually work in fine granularity by modeling word co-occurrence within small sliding windows. With the aim of deriving latent se...
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NTT DOCOMO Technical Journal Vol. 13 No. 3 ©2011 NTT DOCOMO, INC. Copies of articles may be reproduced only for personal, noncommercial use, provided that the name NTT DOCOMO Technical Journal, the name(s) of the author(s), the title and date of the article appear in the copies. *1 latent topic model: A model widely used in document categorization based on the concept that a document is generat...
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Latent Dirichlet allocation (LDA) is a popular unsupervised technique for topic modeling. It learns a generative model which can discover latent topics given a collection of training documents. In the unsupervised learning framework, where the class label is unavailable, it is less intuitive to evaluate the goodness-of-fit and degree of overfitting of learned model. We discuss two measurements ...
متن کاملLatent Dirichlet Allocation For Text And Image Topic Modeling
Latent Dirichlet Allocation (LDA) is a generative model for text documents. It is an unsupervised method which can learn latent topics from documents. We investigate the task of topic modeling of documents using LDA, where the parameters are trained with collapsed Gibbs sampling. Since the training process is unsupervised and the true labels of the training documents are absent, it is hard to m...
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ژورنال
عنوان ژورنال: DEStech Transactions on Computer Science and Engineering
سال: 2017
ISSN: 2475-8841
DOI: 10.12783/dtcse/aice-ncs2016/5647