The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 53-687
Full Name: myat wai
Position: Lecturer
Age: ON
Sex: Female
Address: University of Computer Studies,BanMaw
Country: MYANMAR
Tel: 067-404183
Tel prefix: 095
Fax: 067-404364
E-mail address: wmyonwesit@gmail.com
Other E-mails:
Title of the Paper: Classification Based Automatic Information Extraction System from Free Text
Authors as they appear in the Paper: Myat myo nwe Wai
Email addresses of all the authors: wmyonwesit@gmail.com
Number of paper pages: 10
Abstract: 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 rival 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%.
Keywords: Information Extraction, Machine Learning. Random Forest Classifier, Automatic Extraction System, Data Sparseness Problem, Adaptive Extraction Engine
EXTENSION of the file: .doc
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