Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICMLA.2011.31
Title: An experimental study to investigate the use of additional classifiers to improve information extraction accuracy
Authors: Lek, H.H.
Poo, D.C.C. 
Keywords: information extraction
maximum entropy
natural-language processing
Issue Date: 2011
Source: Lek, H.H.,Poo, D.C.C. (2011). An experimental study to investigate the use of additional classifiers to improve information extraction accuracy. Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011 1 : 412-415. ScholarBank@NUS Repository. https://doi.org/10.1109/ICMLA.2011.31
Abstract: In this paper, we present an information extraction system and investigate the use of additional classifiers to help improve information extraction performance. We propose a simple idea of training an additional classifier using the same feature configurations on another corpus and then using this new classifier to classify the original dataset. The classification result of this new classifier is then used as a feature to the original classifier. We tested this approach on the CMU seminar announcements and the Austin job posting datasets and obtained results better than all previously reported systems. © 2011 IEEE.
Source Title: Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011
URI: http://scholarbank.nus.edu.sg/handle/10635/42775
ISBN: 9780769546070
DOI: 10.1109/ICMLA.2011.31
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