Nguyen Kim Anh

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As the number of documents has been rapidly increasing in recent time, automatic text categorization is becoming a more important and fundamental task in information retrieval and text mining. Accuracy and interpretability are two important aspects of a text classifier. While the accuracy of a classifier measures the ability to correctly classify unseen(More)
Automatic text summarization plays an important role in information retrieval and text mining. Furthermore, it provides an useful solution to the information overload problem. In this paper, we propose a simplicial NMF-based unsupervised generic document summarization method which can inherit some advantages of simplicial NMF such as easy interpretability,(More)
In order to improve the usability of a search engines, Query Suggestion, a technique for generating alternative queries to Web users, has become an indispensable feature for such systems. All major web-search engines and most existing works on query suggestion utilize query logs to determine possible query suggestions. However, for many search systems,(More)
Document clustering has become an increasingly important technique for unsupervised document organization, automatic topic extraction, and fast information retrieval or filtering. This paper proposes a Dirichlet process mixture (DPM) model approach to clustering directional data based on the von Mises-Fisher (vMF) distribution, which arises naturally for(More)
Document classifications is essential to information retrieval and text mining. In real life, unlabeled data is readily available whereas labeled ones are often laborious, expensive and slow to obtain. This paper proposes a novel Document Classification approach based on semi-supervised vMF mixture model on document manifold, called Laplacian regularized(More)