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• W2153962014 abstract "Text retrieval systems typically produce a ranking of documents and let a user decide how far down that ranking to go. In contrast, programs that filter text streams, software that categorizes documents, agents which alert users, and many other IR systems must make decisions without human input or supervision. It is important to define what constitutes good effectiveness for these autonomous systems, tune the systems to achieve the highest possible effectiveness, and estimate how the effectiveness changes as new data is processed. We show how to do this for binary text classification systems, emphasizing that different goals for the system le ad to different optimal behaviors. Optimizing and estimating effectiveness is greatly aided if classifiers that explicitly estimate the probability of class membership are used. Ranked retrieval is the information retrieval (IR) researc her’s favorite tool for dealing with information overload. Ranked retrieval systems display documents in order of probability of releva nce or some similar measure. Users see the best documents first, anddecide how far down the ranking to go in examining the available information. The central role played by ranking in this appr oach has led researchers to evaluate IR systems primarily, often exclusively, on the quality of their rankings. (See, for instance , the TREC evaluations [1].) In some IR applications, however, ranking is not enough: A company provides an SDI (selective dissemination of information) service which filters newswire feeds. Relevant articles are faxed each morning to clients. Interaction between customer and system takes place infrequently. The cost of resources (tying up phone lines, fax machine paper, etc.) is a factor to consider in operating the system. A text categorization system assigns controlled vocabulary categories to incoming documents as they are stored in a text database. Cost cutting has eliminated manual checking of category assignments." @default.
• W2153962014 created "2016-06-24" @default.
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• W2153962014 date "1995-01-01" @default.
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• W2153962014 title "Evaluating and optimizing autonomous text classification systems" @default.
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• W2153962014 doi "https://doi.org/10.1145/215206.215366" @default.
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