An experimental study to investigate the use of additional classifiers to improve information extraction accuracy
Lek, H.H. Poo, D.C.C.
Lek, H.H.
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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.
Keywords
information extraction, maximum entropy, natural-language processing
Source Title
Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011
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Date
2011
DOI
10.1109/ICMLA.2011.31
Type
Conference Paper