Research of Machine Learning Method for Specific Information Recognition on the Internet

With the available resources on the Internet becoming plentiful, a large amount of harmfulinformation is permeating in and has been influencing people's normal work and living seriously. Therefore, some harmful data stream must be recognized and filtered out effectively.After analyzing some har...

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Bibliographic Details
Main Author: Zheng, Dequan
Format: Conference Proceeding
Language:English
Subjects:
Character recognition
Computing methodologies
Computing methodologies > Artificial intelligence
Computing methodologies > Artificial intelligence > Natural language processing
Hardware
Hardware > Power and energy
Hardware > Power and energy > Power estimation and optimization
Hardware > Power and energy > Power estimation and optimization > Platform power issues
Humans
Information analysis
Information filtering
Information filters
Information resources
Internet
IP networks
Learning systems
Testing
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Summary:With the available resources on the Internet becoming plentiful, a large amount of harmfulinformation is permeating in and has been influencing people's normal work and living seriously. Therefore, some harmful data stream must be recognized and filtered out effectively.After analyzing some harmful contents in Internet information stream, we present a new method, which recognizes specific information by Machine Learning (ML). We extracted key information from a number of corpuses through ML method to obtain the part of speech (POS) Transfer-Form for key information by learning from corpuses, which is based on the same pronunciation matching of key information. Further more, the testing value of key information will be obtained in real corpus to examine the likelihood between matching rules from information streams and those learnt from corpuses through the average value of POS transfer probability of key information. Therefore, the testing value for the whole real data stream will be obtained. The experiment proved that the method was efficient for recognizing certainInternet harmful information.
DOI:10.1109/ICMI.2002.1166998