Use of interpretable evolved search query classifiers for sinhala documents

KANKANAMALAGE, Prasanna Haddela, HIRSCH, Laurence, BRUNSDON, Teresa and GAUDOIN, Jotham (2020). Use of interpretable evolved search query classifiers for sinhala documents. In: ARAI, K., KAPOOR, S. and BHATIA, R., (eds.) Proceedings of the Future Technologies Conference (FTC) 2020,. Advances in Intelligent Systems and Computing, 1 . Springer International Publishing, 790-804.

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    Document analysis is a well matured yet still active research field, partly as a result of the intricate nature of building computational tools but also due to the inherent problems arising from the variety and complexity of human languages. Breaking down language barriers is vital in enabling access to a number of recent technologies. This paper investigates the application of document classification methods to new Sinhalese datasets. This language is geographically isolated and rich with many of its own unique features. We will examine the interpretability of the classification models with a particular focus on the use of evolved Lucene search queries generated using a Genetic Algorithm (GA) as a method of document classification. We will compare the accuracy and interpretability of these search queries with other popular classifiers. The results are promising and are roughly in line with previous work on English language datasets.

    Item Type: Book Section
    Additional Information: Series Online ISSN 2194-5365
    Identification Number:
    Page Range: 790-804
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 09 Nov 2020 15:17
    Last Modified: 17 Mar 2021 20:46

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