Word association norms, mutual information, and lexicography
Computational Linguistics
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Text Document Categorization by Term Association
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
The Journal of Machine Learning Research
An analysis of the relative hardness of Reuters-21578 subsets: Research Articles
Journal of the American Society for Information Science and Technology
Integrating word relationships into language models
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Transferring naive bayes classifiers for text classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Combining naive bayes and n-gram language models for text classification
ECIR'03 Proceedings of the 25th European conference on IR research
Modeling term associations for ad-hoc retrieval performance within language modeling framework
ECIR'07 Proceedings of the 29th European conference on IR research
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Weighted average pointwise mutual information for feature selection in text categorization
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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Text classification (TC) has long been an important research topic in information retrieval (IR) related areas. In the literature, the bag-of-words (BoW) model has been widely used to represent a document in text classification and many other applications. However, BoW, which ignores the relationships between terms, offers a rather poor document representation. Some previous research has shown that incorporating language models into the naive Bayes classifier (NBC) can improve the performance of text classification. Although the widely used N -gram language models (LM) can exploit the relationships between words to some extent, they cannot model the long-distance dependencies of words. In this paper, we study the term association modeling approach within the translation LM framework for TC. The new model is called the term association translation model (TATM). The innovation is to incorporate term associations into the document model. We employ the term translation model to model such associative terms in the documents. The term association translation model can be learned based on either the joint probability (JP) of the associative terms through the Bayes rule or the mutual information (MI) of the associative terms. The results of TC experiments evaluated on the Reuters-21578 and 20newsgroups corpora demonstrate that the new model implemented in both ways outperforms the standard NBC method and the NBC with a unigram LM.