The University of Rostock is seeking proposals on Machine Learning and Data Mining for Digital Scholarly Editions for its conference, to be held on-site in Northern Germany from 9-10 June 2022.
The main purpose of the conference is to foster the discussion on Machine Learning and Data Mining techniques in the area of Digital Scholarly Editing. The following questions can be addressed:
– Where can machine learning and data mining be usefully and meaningfully applied in a digital scholarly editing workflow?
– How are machine learning and data mining already used for the creation of digital scholarly editions and what are potential use cases for the future?
– What are challenges in digital scholarly editing that can be successfully addressed by using machine learning and data mining?
– Do editions pose special challenges to the application of machine learning and data mining that need to be overcome?
– What are biases or side effects when applying machine learning and data mining methods to historical data/texts?
– How does the use of machine learning and data mining change the way editors work and the way editions are created? Does it change the role of the editor? How does it change the methods of editing?
– How does digital scholarly editing relate to other digital humanities subfields regarding the application of machine learning and data mining?
– How can a critical engagement with machine learning and data mining techniques in digital scholarly editing be developed and encouraged?
The organizers are interested in a wide range of topics where machine learning and data mining can be used in the digital scholarly editing workflow, for example pattern recognition in image analysis, OCR, NLP (tokenization, lemmatization, part-of-speech tagging, NER), topic modeling, sentiment analysis, clustering and classification tasks which prepare transcription, interpretation, text constitution, annotation, and commentary.
Papers of 4,000 to 6,000 words (not counting the bibliographic references) should be submitted in English to email@example.com as .odt or .docx by 10 February 2022. We only accept papers in English. Please see the submission guidelines.