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 It is not intended as a vendor guide (such things are quickly out of date), or as a recipe for building applications (such recipes are very context-dependent).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 31
... common , because identifying sentence , or even paragraph , boundaries is not trivial . However , documents are often broken into fields by mark up , and then users are allowed to search within a field . The contents of such fields ...
... common , because identifying sentence , or even paragraph , boundaries is not trivial . However , documents are often broken into fields by mark up , and then users are allowed to search within a field . The contents of such fields ...
Page 157
... common collection is necessary . However , a common collection does not ensure that results will be comparable . Indeed , previously published results may not use the same performance metrics , nor the same variant of the collection ...
... common collection is necessary . However , a common collection does not ensure that results will be comparable . Indeed , previously published results may not use the same performance metrics , nor the same variant of the collection ...
Page 182
... common words , listed in an English lexicon . About 170 of these were actually used as proper names , while 10 common words were not in the lexicon . Thus , using a lexicon as the sole guide for recognizing common words as non - names ...
... common words , listed in an English lexicon . About 170 of these were actually used as proper names , while 10 common words were not in the lexicon . Thus , using a lexicon as the sole guide for recognizing common words as non - names ...
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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