Generalized vector spaces model in information retrieval
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Similarity Model and Term Association for Document Categorization
NLDB '02 Proceedings of the 6th International Conference on Applications of Natural Language to Information Systems-Revised Papers
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Survey of Text Mining II: Clustering, Classification, and Retrieval
Survey of Text Mining II: Clustering, Classification, and Retrieval
Document representation using global association distance model
ECIR'07 Proceedings of the 29th European conference on IR research
Similarity measures in documents using association graphs
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
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Text information processing depends critically on the proper document representation. Traditional models, like vector space model, have significant limitations because they do not consider semantic relations amongst terms. In this paper we analyze a document representation that uses an association graph scheme model called Global Association Distance Model or GADM, the significance of the formal distance for the association strength, and the application of several distance-strength functions in this model. We evaluate this significance for topic classification tasks.