Lemmatisation for under-resourced languages with sequence-to-sequence learning: A case of Early Irish

P. 113–124.

Dereza O.

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 data.

Keywords: artificial neural networks, NLP, automatic morphological analysis, Early Irish, under-resourced languages, lemmatisation, sequence-to-sequence models

In book

Proceedings of Third Workshop "Computational linguistics and language science"

Wohlgenannt G., von Waldenfels R., Toldova S., Rakhilina E. V., Lyashevskaya O., Loukachevitch N. V., Artemova E. Issue 4. , Manchester: EasyChair, 2019.