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Lemmatization for Ancient Languages: Rules or Neural Networks?

  • Oksana Dereza
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 930)

Abstract

Lemmatisation, which is one of the most important stages of text preprocessing, consists in grouping the inflected forms of a word together so they can be analysed as a single item. This task is often considered solved for most modern languages irregardless of their morphological type, but the situation is dramatically different for ancient languages. Rich inflectional system and high level of orthographic variation common to these languages together with lack of resources make lemmatising historical data a challenging task. It becomes more and more important as manuscripts are being extensively digitized now, but still remains poorly covered in literature. In this work, I compare a rule-based and a neural network based approach to lemmatisation in case of Early Irish (Old and Middle Irish are often described together as “Early Irish”) data.

Keywords

Early Irish Natural language processing Under-resourced languages Lemmatisation Neural networks Sequence-to-sequence learning 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.National Research University “Higher School of Economics”MoscowRussia
  2. 2.Lomonosov Moscow State UniversityMoscowRussia

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