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Information Processing and Management: an International Journal
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Information Retrieval
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IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Narrative text classification for automatic key phrase extraction in web document corpora
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Data & Knowledge Engineering
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AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
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INES'10 Proceedings of the 14th international conference on Intelligent engineering systems
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
Transactions on Computational Systems Biology II
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Proceedings of the 14th Annual International Conference on Electronic Commerce
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International Journal of Intelligent Information Technologies
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To tackle the issue of information overload, we present an Information Gain-based KeyPhrase Extraction System, called KPSpotter. KPSpotter is a flexible web-enabled keyphrase extraction system, capable of processing various formats of input data, including web data, and generating the extraction model as well as the list of keyphrases in XML. In KPSpotter, the following two features were selected for training and extracting keyphrases: 1) TF*IDF and 2) Distance from First Occurrence. Input training and testing collections were processed in three stages: 1) Data Cleaning, 2) Data Tokenizing, and 3) Data Discretizing. To measure the system performance, the keyphrases extracted by KPSpotter are compared with the ones that the authors assigned. Our experiments show that the performance of KPSpotter was evaluated to be equivalent to KEA, a well-known keyphrase extraction system. KPSpotter, however, is differentiated from other extraction systems in the followings: First, KPSpotter employs a new keyphrase extraction technique that combines the Information Gain data mining measure and several Natural Language Processing techniques such as stemming and case-folding. Second, KPSpotter is able to process various types of input data such as XML, HTML, and unstructured text data and generate XML output. Third, the user can provide input data and execute KPSpotter through the Internet. Fourth, for efficiency and performance reason, KPSpotter stores candidate keyphrases and its related information such as frequency and stemmed form into an embedded database management system.