The nature of statistical learning theory
The nature of statistical learning theory
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Revealing the structure of medical dictations with conditional random fields
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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In this paper, we describe the use of lexical and semantic features for topic classification in dictated medical reports. First, we employ SVM classification to assign whole reports to coarse work-type categories. Afterwards, text segments and their topic are identified in the output of automatic speech recognition. This is done by assigning work-type-specific topic labels to each word based on features extracted from a sliding context window, again using SVM classification utilizing semantic features. Classifier stacking is then used for a posteriori error correction, yielding a further improvement in classification accuracy.