Natural Language Processing for Online Applications: Text Retrieval, Extraction and Categorization
This 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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ACM SIGIR Conference algorithm analysis annotation applications approach assigned associated attempts automatic called Chapter classifiers clusters collection combination common companies computed Conference Conference on Research contain context court decision defined derived described Development in Information document effective entity estimate evaluation event example expressions extraction finding formal frequency given groups human identify indexing Information Retrieval interest kind knowledge language processing learning linguistic look machine match meaning measure methods multiple names Natural Language 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