Natural Language Processing for Online Applications: Text Retrieval, Extraction and CategorizationThis text covers the technologies of document retrieval, information extraction, and text categorization in a way which highlights commonalities in terms of both general principles and practical concerns. It assumes some mathematical background on the part of the reader, but the chapters typically begin with a non-mathematical account of the key issues. Current research topics are covered only to the extent that they are informing current applications; detailed coverage of longer term research and more theoretical treatments should be sought elsewhere. There are many pointers at the ends of the chapters that the reader can follow to explore the literature. However, the book does maintain a strong emphasis on evaluation in every chapter both in terms of methodology and the results of controlled experimentation. |
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... name recognizers 16 1.3.4 Parsers and grammars 17 1.4 Plan of the book 20 CHAPTER 2 Document retrieval 2.1 Information retrieval 24 2.2 Indexing technology 25 2.3 Query processing 27 2.3.1 Boolean search 27 2.3.2 Ranked retrieval 30 2.3 ...
... name recognizers 16 1.3.4 Parsers and grammars 17 1.4 Plan of the book 20 CHAPTER 2 Document retrieval 2.1 Information retrieval 24 2.2 Indexing technology 25 2.3 Query processing 27 2.3.1 Boolean search 27 2.3.2 Ranked retrieval 30 2.3 ...
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ACM SIGIR Conference algorithm analysis annotation Annual International ACM applications approach assigned associated automatic called Chapter classifiers clusters collection combination companies Computational Conference Conference on Research contain context court decision defined Development in Information document effective entity estimate evaluation event example expressions extraction finding formal frequency given groups human identify indexing Information Retrieval interest International ACM SIGIR kind knowledge learning linguistic look machine match meaning measure methods multiple names noun occur parse patterns performance person phrases positive precision Press probability problem Proceedings query question ranking recall recognize refer relevant represent Research and Development rules scores search engine selection semantic sentence Sidebar similar simple space statistical structure summary Table task techniques template term text categorization Token topic typically University vector verb weights words