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In this paper we consider two sequence tagging tasks for medieval Latin:part-of-speech tagging and lemmatization. These are both basic, yetfoundational preprocessing steps in applications such as text re-use detection.Nevertheless, they are generally complicated by the considerable orthographicvariation which is typical of medieval Latin. In Digital Classics, these tasksare traditionally solved in a (i) cascaded and (ii) lexicon-dependent fashion.For example, a lexicon is used to generate all the potential lemma-tag pairsfor a token, and next, a context-aware PoS-tagger is used to select the mostappropriate tag-lemma pair. Apart from the problems with out-of-lexicon items,error percolation is a major downside of such approaches. In this paper weexplore the possibility to elegantly solve these tasks using a single,integrated approach. For this, we make use of a layered neural networkarchitecture from the field of deep representation learning.

Integrated Sequence Tagging for Medieval Latin Using Deep Representation Learning