We explore supervised learning for multi-class, multi-label text classification, focusing on real-world settings, where the distribution of labels changes dynamically over time. We use the PULS Information Extraction system to collect information about the distribution of class labels over named entities found in text. We then combine a knowledge-based rote classifier with statistical classifiers to obtain better performance than either classification method alone. The resulting classifier yields a significant improvement in macro-averaged F-measure compared to the state of the art, while maintaining comparable microaverage.
CITATION STYLE
Du, M., Pierce, M., Pivovarova, L., & Yangarber, R. (2015). Improving supervised classification using information extraction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9103, pp. 3–18). Springer Verlag. https://doi.org/10.1007/978-3-319-19581-0_1
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