publication . Thesis . 2013

Unsupervised topic discovery by anomaly detection

Cheng, Leon;
Open Access
  • Published: 01 Sep 2013
  • Publisher: Monterey, California: Naval Postgraduate School
Abstract
With the vast amount of information and public comment available online, it is of increasing interest to understand what is being said and what topics are trending online. Government agencies, for example, want to know what policies concern the public without having to look through thousands of comments manually. Topic detection provides automatic identification of topics in documents based on the information content and enhances many natural language processing tasks, including text summarization and information retrieval. Unsupervised topic detection, however, has always been a difficult task. Methods such as Latent Dirichlet Allocation (LDA) convert documents...
Subjects
free text keywords: Unsupervised topic detection, Anomaly detection, K-means clustering, Latent Dirichlet Allocation
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