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  • × author_ss:"Kekäläinen, J."
  • × theme_ss:"Retrievalalgorithmen"
  1. Kekäläinen, J.: Binary and graded relevance in IR evaluations : comparison of the effects on ranking of IR systems (2005) 0.02 
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    Abstract
    In this study the rankings of IR systems based on binary and graded relevance in TREC 7 and 8 data are compared. Relevance of a sample TREC results is reassessed using a relevance scale with four levels: non-relevant, marginally relevant, fairly relevant, highly relevant. Twenty-one topics and 90 systems from TREC 7 and 20 topics and 121 systems from TREC 8 form the data. Binary precision, and cumulated gain, discounted cumulated gain and normalised discounted cumulated gain are the measures compared. Different weighting schemes for relevance levels are tested with cumulated gain measures. Kendall's rank correlations are computed to determine to what extent the rankings produced by different measures are similar. Weighting schemes from binary to emphasising highly relevant documents form a continuum, where the measures correlate strongly in the binary end, and less in the heavily weighted end. The results show the different character of the measures.
  2. Sormunen, E.; Kekäläinen, J.; Koivisto, J.; Järvelin, K.: Document text characteristics affect the ranking of the most relevant documents by expanded structured queries (2001) 0.01 
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    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.

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