Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Modern Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Application of information retrieval techniques to single writer documents
Pattern Recognition Letters
COMBINING MULTIPLE CLASSIFIERS USING DEMPSTER'S RULE FOR TEXT CATEGORIZATION
Applied Artificial Intelligence
Neighbor-weighted K-nearest neighbor for unbalanced text corpus
Expert Systems with Applications: An International Journal
A study on optimal parameter tuning for Rocchio text classifier
ECIR'03 Proceedings of the 25th European conference on IR research
Temporally-aware algorithms for document classification
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Pairwise optimized Rocchio algorithm for text categorization
Pattern Recognition Letters
Text categorization with class-based and corpus-based keyword selection
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
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In this paper we investigate whether conventional text categorization methods may suffice to infer different verbal intelligence levels. This research goal relies on the hypothesis that the vocabulary that speakers make use of reflects their verbal intelligence levels. Automatic verbal intelligence estimation of users in a spoken language dialog system may be useful when defining an optimal dialog strategy by improving its adaptation capabilities. The work is based on a corpus containing descriptions (i.e. monologs) of a short film by test persons yielding different educational backgrounds and the verbal intelligence scores of the speakers. First, a one-way analysis of variance was performed to compare the monologs with the film transcription and to demonstrate that there are differences in the vocabulary used by the test persons yielding different verbal intelligence levels. Then, for the classification task, the monologs were represented as feature vectors using the classical TF-IDF weighting scheme. The Naive Bayes, k-nearest neighbors and Rocchio classifiers were tested. In this paper we describe and compare these classification approaches, define the optimal classification parameters and discuss the classification results obtained.