کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
403144 677057 2007 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Variable space hidden Markov model for topic detection and analysis
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Variable space hidden Markov model for topic detection and analysis
چکیده انگلیسی

Discovering topics from large amount of documents has become an important task recently. Most of the topic models treat document as a word sequence, whether in discrete character or term frequency form. However, the number of words in a document is greatly different from that in other documents. This will lead to several problems for current topic models in dealing with topics analysis. On the other hand, it is difficult to perform topic transition analysis based on current topic models. In an attempt to overcome these deficiencies, a variable space hidden Markov model (VSHMM) is proposed to represent the topics, and several operations based on space computation are presented. A hierarchical clustering algorithm with dynamically changing of the component number in topic model is proposed to demonstrate the effectiveness of the VSHMM. Method of document partition based on topic transition is also present. Experiments on a real-world dataset show that the VSHMM can improve the accuracy while decreasing the algorithm’s time complexity greatly compared with the algorithm based on current mixture model.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 20, Issue 7, October 2007, Pages 607–613
نویسندگان
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