Integrated Sequence Tagging for Medieval Latin Using Deep Representation Learning
Mike Kestemont|Jeroen De Gussem
In this paper we consider two sequence tagging tasks for medieval Latin: part-of-speech and lemmatization. These are both basic, yet foundational preprocessing steps in applications such as text re-use detection. Nevertheless, they generally complicated by the considerable orthographic variation which is typical of Latin. Digital Classics, these traditionally solved a (i) cascaded (ii) lexicon-dependent fashion. For example, lexicon used to generate all potential lemma-tag pairs token, next, context-aware PoS-tagger select most appropriate tag-lemma pair. Apart from problems with out-of-lexicon items, error percolation major downside approaches. explore possibility elegantly solve using single, integrated approach. this, make use layered neural network architecture field deep representation learning.
Journal of Data Mining and Digital Humanities|HAL (Le Centre pour la Communication Scientifique Directe)|arXiv (Cornell University)|DOAJ (DOAJ: Directory of Open Access Journals)|HAL (Le Centre pour la Communication Scientifique Directe)|HAL (Le Centre pour la Communication Scientifique Directe)|HAL (Le Centre pour la Communication Scientifique Directe)|Institutional Repository University of Antwerp (University of Antwerp)