Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
KEA: practical automatic keyphrase extraction
Proceedings of the fourth ACM conference on Digital libraries
Learning Algorithms for Keyphrase Extraction
Information Retrieval
Wikify!: linking documents to encyclopedic knowledge
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Wikipedia in Action: Ontological Knowledge in Text Categorization
ICSC '08 Proceedings of the 2008 IEEE International Conference on Semantic Computing
Learning to link with wikipedia
Proceedings of the 17th ACM conference on Information and knowledge management
Understanding user's query intent with wikipedia
Proceedings of the 18th international conference on World wide web
Extracting key terms from noisy and multitheme documents
Proceedings of the 18th international conference on World wide web
Clustering to find exemplar terms for keyphrase extraction
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Experiments in graph-based semi-supervised learning methods for class-instance acquisition
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Open entity extraction from web search query logs
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Conundrums in unsupervised keyphrase extraction: making sense of the state-of-the-art
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Proceedings of the fifth ACM international conference on Web search and data mining
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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.