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 It is not intended as a vendor guide (such things are quickly out of date), or as a recipe for building applications (such recipes are very context-dependent).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 46
... precision and recall Relevant Non - relevant Retrieved Not retrieved a b a + b = m C d c + d = N - m a + c = n b + d = N - n a + b + c + d = N Thus recall can be thought of as the ' hit ratio ' , the proportion of target docu- ments ...
... precision and recall Relevant Non - relevant Retrieved Not retrieved a b a + b = m C d c + d = N - m a + c = n b + d = N - n a + b + c + d = N Thus recall can be thought of as the ' hit ratio ' , the proportion of target docu- ments ...
Page 112
... recall was much more important than precision , so editors would be prepared to tolerate a certain number of false positives in order to ensure high recall . In other applications , such as scanning the news for events of interest , ...
... recall was much more important than precision , so editors would be prepared to tolerate a certain number of false positives in order to ensure high recall . In other applications , such as scanning the news for events of interest , ...
Page 158
... Recall and precision have been adapted to text classification . Precision is the proportion of documents for which the classifier correctly assigned cate- gory c ; and is given by Pi = TP ; mi Recall is the proportion of target document ...
... Recall and precision have been adapted to text classification . Precision is the proportion of documents for which the classifier correctly assigned cate- gory c ; and is given by Pi = TP ; mi Recall is the proportion of target document ...
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Common terms and phrases
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