Natural Language Processing for Online Applications: Text Retrieval, Extraction, and Categorization
John Benjamins Publishing, Jan 1, 2002 - Computational linguistics - 225 pages
This text covers the emerging technologies of document retrieval, information extraction, and text categorization in a way which highlights commonalities in terms of both general principles and practical issues. It seeks to satisfy a need on the part of technology practitioners in the Internet space, faced with having to make difficult decisions as to what research has been done an what the best practices are. It is not intended as a vendor guide (such things are quickly out of date), or as a recipe for building applications (such recipes are very context-dependent). But it does identify the key technologies, the issues involved, and the strengths and weaknesses on evaluation in every chapter, both in terms of methodology (how to evaluate) and what controlled experimentation and industrial experience have to tell us.
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algorithm analysis anaphora applications approach assigned automatic Boolean Chapter classifiers cluster collection combination computed Conference contain context COREF coreference court CYK algorithm decision tree docu document retrieval estimate evaluation example FASTUS finite frequency FSMs given grammar identify information extraction Information Retrieval linear classifiers linguistic Machine Learning match measure Message Understanding Conference methods Microsoft Na´ve Bayes named entity Natural Language Processing noun groups noun phrase occur parser parsing patterns performance probabilistic probability problem Proceedings pronoun proper names query expansion query terms ranked retrieval recall and precision regular expressions relevance feedback relevant documents represent rules score search engine Section semantic sentence Sidebar simple statistical structure summary syntactic Table tagged taggers task techniques template text categorization text classification text mining tion topic training data TREC typically vector space vector space model weight vector words