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This paper explores the effect of profile based method for classification of Wikipedia XML documents. Our approach builds two profiles, exploiting the whole content, Initial Descriptions and links in the Wikipedia documents. For building profiles we use the negative category information which has shown to perform well for classifying unstructured texts. The performance of Cosine and Fractional Similarity...
Field association (FA) terms form a limited set of discriminating terms that give us the knowledge to identify document fields. The primary goal of this research is to make a system that can imitate the process whereby humans recognize the fields by looking at a few Chinese FA terms in a document. This paper proposes a new approach to build a Chinese FA terms dictionary automatically from Wikipedia...
Tags pose an efficient and effective way of organization of resources, but they are not always available. A technique called SCM/THD investigated in this paper extracts entities from free-text annotations, and using the Lin similarity measure over the WordNet thesaurus classifies them into a controlled vocabulary of tags. Hypernyms extracted from Wikipedia are used to map uncommon entities to Wordnet...
In an increasingly technological world, the Internet is often the primary source of information. Traditional encyclopedias, once the cornerstone of any worthy reference collection, have been replaced by online encyclopedias, many of which utilize open source software (OSS) to create and update content. One of the most popular and successful encyclopedias of this nature is Wikipedia. In fact, Wikipedia...
We present a technique which is able to reliably label words or phrases of an arbitrary document with Wikipedia articles (concepts) best describing their meaning. First it scans the document content, and when it finds a word sequence matching the title of a Wikipedia article, it attaches the article to the constituent word(s). The collected articles are then scored based on three factors: (1) how...
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