C4.5: programs for machine learning
C4.5: programs for machine learning
An empirical study of automated dictionary construction for information extraction in three domains
Artificial Intelligence - Special volume on empirical methods
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Machine Learning
Learning Logical Definitions from Relations
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Machine learning for information extraction in informal domains
Machine learning for information extraction in informal domains
Relational learning via propositional algorithms: an information extraction case study
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
CRYSTAL inducing a conceptual dictionary
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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The ever increasing on-line text information can be made available to automatic processing by information system. Several machine learning techniques have been applied in order to build automatic information that rivaling knowledge engineering approach. The past reporting information extraction system using machine learning techniques that expend the separate classifiers for each category of entities. In this paper, we introduce the new classification based information extraction system utilizing only one Random Forest classifier for all candidate entities to save the computational costs of algorithms. Our approach extends the original idea of Random Forest to deal with the data sparseness problem in information extraction engine. Experimental results of this system indicate that the proposed method can be a practical solution for building extraction system reaching an F-measure as high as 87.5%.