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Expanding Queries with Maximum Likelihood Estimators and Language Models

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Proceedings of the ICR’22 International Conference on Innovations in Computing Research (ICR 2022)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1431))

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Abstract

The employment of various language modelling techniques in the area of information retrieval is gaining wide adoption in the state of the art methods. The precision of the language model enables the solution of the issue of information retrieval in a huge corpus of texts. To accomplish this, these techniques begin by estimating a probabilistic linguistic model for each article in the collection that is capable of generating a ranking of relevant texts in response to a query. One of the difficulties that this family of methods faces is a shortage of data. As a result, smoothing methods capable of changing the maximum likelihood estimator are required to account for the imprecision created. This paper highlights its use surpasses established approaches, such as tf-idf, for creating rankings of documents sorted by relevance. Finally, we examine various ideas related to query expansion by utilizing such methods.

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Authors and Affiliations

  1. Computer Engineering and Informatics Department, University of Patras, 26504, Patras, Greece

    Christos Karras, Aristeidis Karras & Spyros Sioutas

  2. Department of Management Science and Technology, University of Patras, 26334, Patras, Greece

    Leonidas Theodorakopoulos & Ioanna Giannoukou

Authors
  1. Christos Karras
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  2. Aristeidis Karras
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  3. Leonidas Theodorakopoulos
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  4. Ioanna Giannoukou
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  5. Spyros Sioutas
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Corresponding author

Correspondence to Christos Karras .

Editor information

Editors and Affiliations

  1. University of Detroit Mercy, Detroit, MI, USA

    Kevin Daimi

  2. Kent Institute Australia, Sydney, NSW, Australia

    Abeer Al Sadoon

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Karras, C., Karras, A., Theodorakopoulos, L., Giannoukou, I., Sioutas, S. (2022). Expanding Queries with Maximum Likelihood Estimators and Language Models. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the ICR’22 International Conference on Innovations in Computing Research. ICR 2022. Advances in Intelligent Systems and Computing, vol 1431. Springer, Cham. https://doi.org/10.1007/978-3-031-14054-9_20

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