# Integrated Sequence Tagging for Medieval Latin Using Deep Representation Learning

arXiv: https://arxiv.org/abs/1603.01597 · HTML: https://mlsanity.com/paper/1603.01597/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2016.

## Built on by (most cited first, 3 of 3)

- [Corpus and Models for Lemmatisation and POS-tagging of Old French](https://mlsanity.com/paper/2109.11442/) (2021)
- [Latin writing styles analysis with Machine Learning: New approach to old questions](https://mlsanity.com/paper/2109.00601/) (2021)
- [eFontes. Part of Speech Tagging and Lemmatization of Medieval Latin Texts.A Cross-Genre Survey](https://mlsanity.com/paper/2407.00418/) (2024)

## Abstract (arXiv)

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