Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A study on convolution kernels for shallow semantic parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Combined syntactic and semantic Kernels for text classification
ECIR'07 Proceedings of the 29th European conference on IR research
Analysing part-of-speech for portuguese text classification
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
University of Évora in QA@CLEF-2004
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
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Most text classification systems use bag-of-words representation of documents to find the classification target function. Linguistic structures such as morphology, syntax and semantic are completely neglected in the learning process. This paper proposes a new document representation that, while including its context independent sentence meaning, is able to be used by a structured kernel function, namely the direct product kernel. The proposal is evaluated using a dataset of articles from a Portuguese daily newspaper and classifiers are built using the SVM algorithm. The results show that this structured representation, while only partially describing document's significance has the same discriminative power over classes as the traditional bag-of-words approach.