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Now a day, the massive amount of data and information (recently termed as “Big Data”) causes accessibility and retrieval problems if poorly managed. This is due to their relational structure which is more complicate, unexplainable, and unanalyzable with simple or traditional methods. The uniform display of these data and information is also difficult due to their diversified formats. Bag of Words...
We propose a heterogeneous information network mining algorithm: feature-enhanced Rank Class (F-Rank Class). F-Rank Class extends Rank Class to a unified classification framework that can be applied to binary or multiclass classification of unimodal or multimodal data. We experimented on a multimodal document dataset, 2008/9 Wikipedia Selection for Schools. For unimodal classification, F-Rank Class...
Recognition of named entities (people, companies, locations, etc) is an essential task of text analytics. We address the subproblem of this task, namely, named entity classification. We propose a novel approach that constructs an effective fine-grained named entity classifier. Its key highlights are semi-automatic training set construction from Wikipedia articles and additional feature selection....
As a corpus for knowledge extraction, Wikipedia has become one of the promising resources among researchers in various domains such as NLP, WWW, IR and AI since it has a great coverage of concepts for wide-range domain, remarkable accuracy and easy-handled structure for analysis. Relatedness measurement among concepts is one of the traditional research topics on Wikipedia analysis. The value of relatedness...
Text classification has been widely used to assist users with the discovery of useful information from the Internet. However, current text classification systems are based on the ldquoBag of Wordsrdquo (BOW) representation, which only accounts for term frequency in the documents, and ignores important semantic relationships between key terms. To overcome this problem, previous work attempted to enrich...
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