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 It is not intended as a vendor guide (such things are quickly out of date), or as a recipe for building applications (such recipes are very context-dependent).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 51
... kind of vehicle , walking is a kind of moving . The lexicon also records morphological information , so that the Document retrieval 51.
... kind of vehicle , walking is a kind of moving . The lexicon also records morphological information , so that the Document retrieval 51.
Page 52
... kind of color , and turns is an inflected form of the root form turn , which is a kind of change . Woods found that using these knowledge sources added 20 % to the success rate of a state - of - the - art search engine , albeit over a ...
... kind of color , and turns is an inflected form of the root form turn , which is a kind of change . Woods found that using these knowledge sources added 20 % to the success rate of a state - of - the - art search engine , albeit over a ...
Page 180
... kind . Before examining coreference in more detail , it is worth understanding the technology behind named entity recognition , since this is a crucial preparatory step for resolving coreferences accurately . 5.2.1 Named entity ...
... kind . Before examining coreference in more detail , it is worth understanding the technology behind named entity recognition , since this is a crucial preparatory step for resolving coreferences accurately . 5.2.1 Named entity ...
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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