CompareLDA: A Topic Model for Document Comparison
نویسندگان
چکیده
منابع مشابه
A Dynamic Topic Model for Document Segmentation
Factor language models, like Latent Semantic Analysis, represent documents as mixtures of topics, and have a variety of applications. Normally, the mixture is computed at the whole-document level, that is, the entire document contains material on several topics, without specifying where they occur in the document. In this paper, we describe a new model which computes the topic mixture estimate ...
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A method of document comparison based on a hierarchical dictionary of topics (concepts) is described. The hierarchical links in the dictionary are supplied with the weights that are used for detecting the main topics of a document and for determining the similarity between two documents. The method allows for the comparison of documents that do not share any words literally but do share concept...
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Documents are usually represented in the bag-of-word space. However, this representation does not take into account the possible relations between words. We propose here a graphical model for representing documents: the Theme Topic Mixture Model (TTMM). This model assumes two types of relations among textual data. Topics link words to each other and Themes gather documents with particular distr...
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In Automatic Text Processing tasks, documents are usually represented in the bag-ofwords space. However, this representation does not take into account the possible relations between words. We propose here a review of a family of document density estimation models for representing documents. Inside this family we derive another possible model: the Theme Topic Mixture Model (TTMM). This model as...
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0306-4573/$ see front matter 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ipm.2013.05.002 ⇑ Corresponding author at: Department of Computer Science, South China University of Technology, Guangzhou, China. Tel.: +852 39438461; f 26035505. E-mail addresses: [email protected] (P. Yang), [email protected] (W. Gao), [email protected] (Q. Tan), [email protected] (K.-F. Wong)...
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ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2019
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v33i01.33017112