Presentation Open Access
The humanities meet computer science to create new synergies using computer vision and natural language processing.
Aim & Scope
Historians are increasingly using technologies to evaluate digitised texts in a machine-readable way, as well as techniques from the field of natural language processing (NLP) to analyse the content and context of language in written artefacts. These techniques can be used to analyse large corpora and identify patterns. In general, however, these methods often use training data from current rather than historical data. The use of these methods can lead to biases in the historical record, incurring the risk of false inferences about history. Therefore, the methods used should be fully investigated to account for any biases. In this DL workshop, the challenges of applying computer vision and NLP techniques in the humanities, and first solutions to them, will be presented.
This entry includes the following presentations from the first Data Linking Workshop 2023: Computer Vision and Natural Language Processing – Challenges in the Humanities
The submitted presentations are included in this upload for which permission to publish has been granted.
The KI2021 workshop – Humanities-Centred AI was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2176 ’Understanding Written Artefacts: Material, Interaction and Transmission in Manuscript Cultures’, project no. 390893796.
Name | Size | |
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00_DataLinkingWorkshop2023-Agenda.pdf
md5:9b2cbccce86bb7bed1e5f93fc262a05c |
186.7 kB | Download |
01-Welcome-text_Pepper.pdf
md5:32e214d14b5602f30badc5ed8bae53e8 |
63.0 kB | Download |
01-Welcome_Pepper.mp4
md5:5d7ca1f4605f1623b4fb7f9d054529d5 |
64.2 MB | Download |
02-Keynote1_Tamilex_Wilden_Li.pdf
md5:aacc5e192f98a57639333fc64b68e968 |
1.9 MB | Download |
03-READ_Baums-White.pdf
md5:0557c41bc4df7a4c0d47047720a8e1f4 |
4.7 MB | Download |
04-BuBronIn_Hinueber-Hu-Melzer.pdf
md5:06ac40309baa4e7a3e48fbc19b39b101 |
2.8 MB | Download |
05-Proto-Śāradā_Holz.pdf
md5:62a97359daef637ec982e3ed055de5ec |
5.5 MB | Download |
06a-Taxonomy_Konczak-Nagel.pdf
md5:4c39db6a6bf827fcc6ebca81f5194137 |
12.2 MB | Download |
06b_Deep_Learning _KMIS-Radisch.pdf
md5:105180fc33beee0338509b26241dc608 |
7.5 MB | Download |
07-Keynote2_Aligned-AI_Moeller.pdf
md5:94429cfa868499a0010629a9eee5f339 |
2.8 MB | Download |
08-Greek-papyri_Marthot-Santaniello.pdf
md5:f38535cc6c028d30c11524ccfa3a77f4 |
7.9 MB | Download |
09-Newari_Serbaeva.pdf
md5:d4414e58a55a1f7d0e2a3853469375f8 |
13.1 MB | Download |
10-OCR_Hinrichsen.pdf
md5:c38b112b17c7112e14e0329cdbab6477 |
1.2 MB | Download |
12-PersistentData_Schiff_Moeller.pdf
md5:6c6b0e940636123f37fe36221a77bbc7 |
1.4 MB | Download |
13-FindingAndLinking_HMYR.pdf
md5:d04dcf7b78fbabd807aa94f631535efd |
3.2 MB | Download |
14-InfSoD_Melzer.pdf
md5:577bacc4dad16005f58fc7aedbaf1b22 |
1.3 MB | Download |
15-Digital_Paleography_Koller.pdf
md5:7412e864ac81abd6ed56d3153f25a6f6 |
3.3 MB | Download |
16-ComputerVision_Mohammed.pdf
md5:91866e50669f14c941755640e1ec8b74 |
1.6 MB | Download |
17-Bye_Pepper.mp4
md5:10e61f25edecc9c00daa32606a66efee |
53.3 MB | Download |
17-Bye_Pepper.pdf
md5:b26693a3ee7a4cf024b8eb2482c680ea |
59.4 kB | Download |