Probabilistic models in information retrieval
The Computer Journal - Special issue on information retrieval
Two models of retrieval with probabilistic indexing
Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
Clumping properties of content-bearing words
Journal of the American Society for Information Science
Foundations of statistical natural language processing
Foundations of statistical natural language processing
A probabilistic model of information retrieval: development and comparative experiments Part 2
Information Processing and Management: an International Journal
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Extracting significant words from corpora for ontology extraction
Proceedings of the 3rd international conference on Knowledge capture
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Determining termhood for learning domain ontologies using domain prevalence and tendency
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Measuring data-driven ontology changes using text mining
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Constructing Web Corpora through Topical Web Partitioning for Term Recognition
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Keyword search considering user's preference in P2P networks
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
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Many existing techniques for term extraction are heuristically-motivated and criticised as ad-hoc. The definitions and assumptions critical to set the boundary for the effectiveness of the techniques are often implicit and unclear. Here we present a probabilistic framework for measuring termhood to address the lack of mathematical foundation in existing techniques.