ConsentCanvas: automatic texturing for improved readability in End-User License Agreements
HLT-SS '11 Proceedings of the ACL 2011 Student Session
Hierarchical user interest modeling for Chinese web pages
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
Personalized search results with user interest hierarchies learnt from bookmarks
WebKDD'05 Proceedings of the 7th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
Identifying well-formed biomedical phrases in MEDLINE® text
Journal of Biomedical Informatics
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Finding meaningful phrases in a document has been studied in various information retrieval systems in order to improve the performance. Many previous statistical phrase-finding methods had a different aim such as document classification. Some are hybridized with statistical and syntactic grammatical methods; others use correlation heuristics between words. We propose a new phrase-finding algorithm that adds correlated words one by one to the phrases found in the previous stage, maintaining high correlation within a phrase. Our results indicate that our algorithm finds more meaningful phrases than an existing algorithm. Furthermore, the previous algorithm could be improved by applying different correlation functions.