Alemayehu, N.: Analysis of performance variation using quey expansion (2003)
0.00
0.002269176 = product of:
0.004538352 = sum of:
0.004538352 = product of:
0.009076704 = sum of:
0.009076704 = weight(_text_:a in 1454) [ClassicSimilarity], result of:
0.009076704 = score(doc=1454,freq=10.0), product of:
0.053105544 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046056706 = queryNorm
0.1709182 = fieldWeight in 1454, product of:
3.1622777 = tf(freq=10.0), with freq of:
10.0 = termFreq=10.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046875 = fieldNorm(doc=1454)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- Information retrieval performance evaluation is commonly made based an the classical recall and precision based figures or graphs. However, important information indicating causes for variation may remain hidden under the average recall and precision figures. Identifying significant causes for variation can help researchers and developers to focus an opportunities for improvement that underlay the averages. This article presents a case study showing the potential of a statistical repeated measures analysis of variance for testing the significance of factors in retrieval performance variation. The TREC-9 Query Track performance data is used as a case study and the factors studied are retrieval method, topic, and their interaction. The results show that retrieval method, topic, and their interaction are all significant. A topic level analysis is also made to see the nature of variation in the performance of retrieval methods across topics. The observed retrieval performances of expansion runs are truly significant improvements for most of the topics. Analyses of the effect of query expansion an document ranking confirm that expansion affects ranking positively.
- Type
- a
