- author = "Ronan Cummins and Colm O'Riordan",
- title = "An Axiomatic Study of Learned Term-Weighting Schemes",
- booktitle = "SIGIR 2007 workshop: Learning to Rank for Information Retrieval",
- year = "2007",
- editor = "Thorsten Joachims and Hang Li and Tie-Yan Liu and ChengXiang Zhai",
- month = "27 " # jul,
- organisation = "Microsoft",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://ww2.it.nuigalway.ie/cirg/localpubs/axioms.pdf",
-
abstract = "At present, there exists many term-weighting schemes
each based on different underlying models of retrieval.
Learn- ing approaches are increasingly being applied to
the term- weighting problem, further increasing the
number of useful term-weighting approaches available.
Many of these term- weighting schemes have certain
features and properties in common. As such, it is
beneficial to formally model these common features and
properties.
In this paper, we introduce a term-weighting scheme that has been developed incrementally using an evolutionary learn- ing approach. We analyse one such term-weighting function produced from the evolutionary approach by decomposing it into inductive query and document growth functions. Con- sequently, we show that it is consistent with a number of axioms previously postulated for term-weighting schemes. Interestingly, we show that a further constraint can be de- rived from the resultant scheme.
Finally, we empirically validate our analysis, and the newly developed constraint, by showing that the newly developed nonparametric term-weighting scheme can outperform BM25 and the pivoted document length normalisation scheme over many different query types and collections. We conclude that the scheme produced from the learning approach adds further evidence to the validity of the axioms.",
- notes = "https://research.microsoft.com/en-us/um/beijing/events/LR4IR-2007/",
