lecture8-evaluation-handout-6-per

84 evaluaon idea if locally precision increases with

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Unformatted text preview: CJ van Rijsbergen, Informa)on Retrieval Introduc)on to Informa)on Retrieval 16 Sec. 8.4 Evalua)ng ranked results   Evalua)on of ranked results:   The system can return any number of results   By taking various numbers of the top returned documents (levels of recall), the evaluator can produce a precision ­ recall curve 17 18 3 Introduc)on to Informa)on Retrieval Sec. 8.4 A precision ­recall curve Introduc)on to Informa)on Retrieval Sec. 8.4 Averaging over queries   A precision ­recall graph for one query isn’t a very sensible thing to look at   You need to average performance over a whole bunch of queries.   But there’s a technical issue:   Precision ­recall calcula)ons place some points on the graph   How do you determine a value (interpolate) between the points? 19 Introduc)on to Informa)on Retrieval Sec. 8.4 Interpolated precision 20 Introduc)on to Informa)on Retrieval Sec. 8.4 Evalua)on   Idea: If locally precision increases with increasing recall, then you should get to count that…   So you take the max of precisions to right of value ...
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This document was uploaded on 02/26/2014.

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