Short-text domain specific key terms/phrases extraction using an n-gram model with Wikipedia
|Short-text domain specific key terms/phrases extraction using an n-gram model with Wikipedia|
|Author(s)||Qureshi M.A., O'Riordan C., Pasi G.|
|Published in||ACM International Conference Proceeding Series|
|Keyword(s)||community detection, n-gram model, open-domain knowledge, wikipedia (Extra: Academic research, Area of interest, Collection of documents, Community detection, Document collection, Domain specific, N-gram models, open-domain knowledge, Wikipedia, Computer aided language translation, Knowledge management, Marine biology, Websites)|
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Short-text domain specific key terms/phrases extraction using an n-gram model with Wikipedia is a 2012 conference paper written in English by Qureshi M.A., O'Riordan C., Pasi G. and published in ACM International Conference Proceeding Series.
Finding domain specific key terms/phrases from a given set of documents is a challenging task. A domain may be defined as an area of interest over a collection of documents which may not be explicitly defined but implicitly observable in those documents. When considering a collection of documents related to academic research, examples of key terms/phrases may be Information Retrieval", "Marine Biology", etc. In this paper a technique for extracting important key terms/phrases in a considered topical domain is proposed using external evidence from the titles of Wikipedia articles and the Wikipedia category graph. We performed some experiments over the document collection of Web sites of different post-graduate schools. Our preliminary evaluations show promising results for the detection of domain specific key terms/phrases from the given set of domain focused Web pages.
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