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Nunc profana tractemus. Detecting Code-Switching in a Large Corpus of 16th Century Letters


Volk, Martin; Fischer, Lukas; Scheurer, Patricia; Schwitter, Raphael; Ströbel, Phillip; Suter, Benjamin (2022). Nunc profana tractemus. Detecting Code-Switching in a Large Corpus of 16th Century Letters. In: Proceedings of LREC-2022, Marseille, 21 June 2022 - 26 June 2022, LREC.

Abstract

This paper is based on a collection of 16th century letters from and to the Zurich reformer Heinrich Bullinger. Around 12,000 letters of this exchange have been preserved, out of which 3100 have been professionally edited, and another 5500 are available as provisional transcriptions. We have investigated code-switching in these 8600 letters, first on the sentence-level and then on the word-level. In this paper we give an overview of the corpus and its language mix (mostly Early New High German and Latin, but also French, Greek, Italian and Hebrew). We report on our experiences with a popular language identifier and present our results when training an alternative identifier on a very small training corpus of only 150 sentences per language. We use the automatically labeled sentences in order to bootstrap a word-based language classifier which works with high accuracy. Our research around the corpus building and annotation involves automatic handwritten text recognition, text normalisation for ENH German, and machine translation from medieval Latin into modern German.

Abstract

This paper is based on a collection of 16th century letters from and to the Zurich reformer Heinrich Bullinger. Around 12,000 letters of this exchange have been preserved, out of which 3100 have been professionally edited, and another 5500 are available as provisional transcriptions. We have investigated code-switching in these 8600 letters, first on the sentence-level and then on the word-level. In this paper we give an overview of the corpus and its language mix (mostly Early New High German and Latin, but also French, Greek, Italian and Hebrew). We report on our experiences with a popular language identifier and present our results when training an alternative identifier on a very small training corpus of only 150 sentences per language. We use the automatically labeled sentences in order to bootstrap a word-based language classifier which works with high accuracy. Our research around the corpus building and annotation involves automatic handwritten text recognition, text normalisation for ENH German, and machine translation from medieval Latin into modern German.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
06 Faculty of Arts > Zurich Center for Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Uncontrolled Keywords:language identification, historical languages, Latin, Early New High German
Language:English
Event End Date:26 June 2022
Deposited On:29 Jun 2022 14:09
Last Modified:20 Jun 2024 03:35
Publisher:LREC
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Official URL:https://lrec2022.lrec-conf.org/en/
Project Information:
  • : FunderUZH Foundation
  • : Grant ID
  • : Project Title
  • Content: Published Version
  • Language: English