@inproceedings{9bf14c5547d940389843ee77e186e7eb,
title = "Short-text domain specific key terms/phrases extraction using an n-gram model with Wikipedia",
abstract = "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.",
keywords = "community detection, n-gram model, open-domain knowledge, wikipedia",
author = "Qureshi, \{M. Atif\} and Colm O'Riordan and Gabriella Pasi",
year = "2012",
doi = "10.1145/2396761.2398680",
language = "English",
isbn = "9781450311564",
series = "ACM International Conference Proceeding Series",
pages = "2515--2518",
booktitle = "CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management",
note = "21st ACM International Conference on Information and Knowledge Management, CIKM 2012 ; Conference date: 29-10-2012 Through 02-11-2012",
}
