-
Halttunen, K.; Sormunen, E.: Learning information retrieval through an educational game : is gaming sufficient for learning? (2000)
0.01
0.008845377 = product of:
0.061917633 = sum of:
0.019976506 = weight(_text_:information in 3865) [ClassicSimilarity], result of:
0.019976506 = score(doc=3865,freq=4.0), product of:
0.052020688 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.029633347 = queryNorm
0.3840108 = fieldWeight in 3865, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.109375 = fieldNorm(doc=3865)
0.04194113 = weight(_text_:retrieval in 3865) [ClassicSimilarity], result of:
0.04194113 = score(doc=3865,freq=2.0), product of:
0.08963835 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.029633347 = queryNorm
0.46789268 = fieldWeight in 3865, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.109375 = fieldNorm(doc=3865)
0.14285715 = coord(2/14)
- Source
- Education for information. 18(2000) no.4, S.289-311
-
Vakkari, P.; Sormunen, E.: ¬The influence of relevance levels an the effectiveness of interactive information retrieval (2004)
0.01
0.0059455284 = product of:
0.041618697 = sum of:
0.0104854815 = weight(_text_:information in 2884) [ClassicSimilarity], result of:
0.0104854815 = score(doc=2884,freq=6.0), product of:
0.052020688 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.029633347 = queryNorm
0.20156369 = fieldWeight in 2884, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046875 = fieldNorm(doc=2884)
0.031133216 = weight(_text_:retrieval in 2884) [ClassicSimilarity], result of:
0.031133216 = score(doc=2884,freq=6.0), product of:
0.08963835 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.029633347 = queryNorm
0.34732026 = fieldWeight in 2884, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.046875 = fieldNorm(doc=2884)
0.14285715 = coord(2/14)
- Abstract
- In this paper, we focus an the effect of graded relevance an the results of interactive information retrieval (IR) experiments based an assigned search tasks in a test collection. A group of 26 subjects searched for four Text REtrieval Conference (TREC) topics using automatic and interactive query expansion based an relevance feedback. The TREC- and user-suggested pools of relevant documents were reassessed an a four-level relevance scale. The results show that the users could identify nearly all highly relevant documents and about half of the marginal ones. Users also selected a fair number of irrelevant documents for query expansion. The findings suggest that the effectiveness of query expansion is closely related to the searchers' success in retrieving and identifying highly relevant documents for feedback. The implications of the results an interpreting the findings of past experiments with liberal relevance thresholds are also discussed.
- Source
- Journal of the American Society for Information Science and Technology. 55(2004) no.11, S.963-969
-
Sormunen, E.; Kekäläinen, J.; Koivisto, J.; Järvelin, K.: Document text characteristics affect the ranking of the most relevant documents by expanded structured queries (2001)
0.00
0.00446759 = product of:
0.031273127 = sum of:
0.010089659 = weight(_text_:information in 4487) [ClassicSimilarity], result of:
0.010089659 = score(doc=4487,freq=8.0), product of:
0.052020688 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.029633347 = queryNorm
0.19395474 = fieldWeight in 4487, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0390625 = fieldNorm(doc=4487)
0.021183468 = weight(_text_:retrieval in 4487) [ClassicSimilarity], result of:
0.021183468 = score(doc=4487,freq=4.0), product of:
0.08963835 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.029633347 = queryNorm
0.23632148 = fieldWeight in 4487, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.0390625 = fieldNorm(doc=4487)
0.14285715 = coord(2/14)
- Abstract
- The increasing flood of documentary information through the Internet and other information sources challenges the developers of information retrieval systems. It is not enough that an IR system is able to make a distinction between relevant and non-relevant documents. The reduction of information overload requires that IR systems provide the capability of screening the most valuable documents out of the mass of potentially or marginally relevant documents. This paper introduces a new concept-based method to analyse the text characteristics of documents at varying relevance levels. The results of the document analysis were applied in an experiment on query expansion (QE) in a probabilistic IR system. Statistical differences in textual characteristics of highly relevant and less relevant documents were investigated by applying a facet analysis technique. In highly relevant documents a larger number of aspects of the request were discussed, searchable expressions for the aspects were distributed over a larger set of text paragraphs, and a larger set of unique expressions were used per aspect than in marginally relevant documents. A query expansion experiment verified that the findings of the text analysis can be exploited in formulating more effective queries for best match retrieval in the search for highly relevant documents. The results revealed that expanded queries with concept-based structures performed better than unexpanded queries or Ñnatural languageÒ queries. Further, it was shown that highly relevant documents benefit essentially more from the concept-based QE in ranking than marginally relevant documents.
