Natural Language Processing for Online Applications: Text Retrieval, Extraction, and CategorizationThis 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 It is not intended as a vendor guide (such things are quickly out of date), or as a recipe for building applications (such recipes are very context-dependent).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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Page 79
... Sidebar 3.2 for a more for- mal specification of these operations and a formal definition of regular expres- sions . ) Sidebar 3.2 Regular languages Regular expressions can be defined formally as sequences over any finite alphabet A ...
... Sidebar 3.2 for a more for- mal specification of these operations and a formal definition of regular expres- sions . ) Sidebar 3.2 Regular languages Regular expressions can be defined formally as sequences over any finite alphabet A ...
Page 102
... Sidebar 3.5 ) . Sidebar 3.5 Heuristics for coping with ambiguity Jackson et al 102 Chapter 3.
... Sidebar 3.5 ) . Sidebar 3.5 Heuristics for coping with ambiguity Jackson et al 102 Chapter 3.
Page 207
... Sidebar 5.4 ) , rather than any kind of gram- matical analysis . During summary construction , more informative sentences will be preferred over less informative ones , and more than one sentence will not be used from the same ...
... Sidebar 5.4 ) , rather than any kind of gram- matical analysis . During summary construction , more informative sentences will be preferred over less informative ones , and more than one sentence will not be used from the same ...
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algorithm analysis anaphora applications approach assigned automatic Boolean Chapter classifiers cluster collection combination computed Conference contain context corefer coreference court decision tree docu document retrieval estimate evaluation example FASTUS filtering finite frequency given grammar identify information extraction information retrieval linear classifiers linguistic Machine Learning match measure Message Understanding Conference methods Microsoft Naïve Bayes named entity Natural Language Processing non-relevant NOT-A-NAME 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 tf-idf tion topic TREC typically vector space vector space model weight vector words