Search (3 results, page 1 of 1)

  • × author_ss:"Baillie, M."
  • × type_ss:"a"
  • × year_i:[2000 TO 2010}
  1. Baillie, M.; Azzopardi, L.; Ruthven, I.: Evaluating epistemic uncertainty under incomplete assessments (2008) 0.01 
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    Abstract
    The thesis of this study is to propose an extended methodology for laboratory based Information Retrieval evaluation under incomplete relevance assessments. This new methodology aims to identify potential uncertainty during system comparison that may result from incompleteness. The adoption of this methodology is advantageous, because the detection of epistemic uncertainty - the amount of knowledge (or ignorance) we have about the estimate of a system's performance - during the evaluation process can guide and direct researchers when evaluating new systems over existing and future test collections. Across a series of experiments we demonstrate how this methodology can lead towards a finer grained analysis of systems. In particular, we show through experimentation how the current practice in Information Retrieval evaluation of using a measurement depth larger than the pooling depth increases uncertainty during system comparison.
  2. Ruthven, I.; Baillie, M.; Azzopardi, L.; Bierig, R.; Nicol, E.; Sweeney, S.; Yaciki, M.: Contextual factors affecting the utility of surrogates within exploratory search (2008) 0.01 
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    Footnote
    Beitrag eines Themenschwerpunktes "Evaluating exploratory search systems"
  3. Bache, R.; Baillie, M.; Crestani, F.: Measuring the likelihood property of scoring functions in general retrieval models (2009) 0.01 
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    Abstract
    Although retrieval systems based on probabilistic models will rank the objects (e.g., documents) being retrieved according to the probability of some matching criterion (e.g., relevance), they rarely yield an actual probability, and the scoring function is interpreted to be purely ordinal within a given retrieval task. In this brief communication, it is shown that some scoring functions possess the likelihood property, which means that the scoring function indicates the likelihood of matching when compared to other retrieval tasks, which is potentially more useful than pure ranking although it cannot be interpreted as an actual probability. This property can be detected by using two modified effectiveness measures: entire precision and entire recall.

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