Original Research

Outomatiese lemma-identifisering vir Afrikaans

H.J. Groenewald, G.B. van Huyssteen
Literator | Vol 29, No 1 | a101 | DOI: https://doi.org/10.4102/lit.v29i1.101 | © 2008 H.J. Groenewald, G.B. van Huyssteen | This work is licensed under CC Attribution 4.0
Submitted: 25 July 2008 | Published: 25 July 2008

About the author(s)

H.J. Groenewald, Sentrum vir Tekstegnologie (CTexT), Potchefstroomkampus, Noordwes-Universiteit, South Africa
G.B. van Huyssteen, Sentrum vir Tekstegnologie (CTexT), Potchefstroomkampus, Noordwes-Universiteit, South Africa

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Abstract

Automatic lemmatisation for Afrikaans

Automatic lemmatisation is a general normalisation procedure in text processing, where all inflected forms of a lexical word are normalised to a single lemma (i.e. a meaningful, uninflected base form from which more complex word forms could be formed). Traditionally, lemmatisers are developed by writing language-specific rules to identify lemmas. In this article an alternative approach is investigated, namely a machine learning approach, to develop a lemmatiser for Afrikaans (LIA: “Lemmaidentifiseerder vir Afrikaans”). An overview regarding the process of inflection in Afrikaans is provided with the aim of identifying the categories of inflection that are relevant for lemmatisation in Afrikaans. The format of the input and output is described with special reference to the nine inflectional categories for Afrikaans that the system should be able to handle. Then the task of lemmatisation as a classification task for machine learning is described, and a concise introduction to memory-based learning is provided. The development and evaluation of LIA is discussed in detail, and it is illustrated how the performance of the initial classifier is improved through feature selection and parameter optimisation. The best classifier reaches an accuracy of 92,8%. The article concludes with a view on some future work.

Keywords

Afrikaans; Feature Selection; Inflection; Lemmatisation; Machine Learning; Morphology; Natural Language Processing; Parameter Optimisation; Text Technology

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