Automatic medieval charters structure detection : A Bi-LSTM linear segmentation approach
This paper presents a model aiming to automatically detect sections in medieval Latin charters. These legal sources are some of the most important sources for medieval studies as they reflect economic and social dynamics as well as legal and institutional writing practices. An automatic linear segme...
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| Vydáno v: | Journal of data mining and digital humanities Ročník 2022 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
INRIA
30.10.2022
Nicolas Turenne |
| Témata: | |
| ISSN: | 2416-5999, 2416-5999 |
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| Abstract | This paper presents a model aiming to automatically detect sections in medieval Latin charters. These legal sources are some of the most important sources for medieval studies as they reflect economic and social dynamics as well as legal and institutional writing practices. An automatic linear segmentation can greatly facilitate charter indexation and speed up the recovering of evidence to support historical hypothesis by the means of granular inquiries on these raw, rarely structured sources. Our model is based on a Bi-LSTM approach using a final CRF-layer and was trained using a large, annotated collection of medieval charters (4,700 documents) coming from Lombard monasteries: the CDLM corpus (11th-12th centuries). The evaluation shows a high performance in most sections on the test-set and on an external evaluation corpus consisting of the Montecassino abbey charters (10th-12th centuries). We describe the architecture of the model, the main problems related to the treatment of medieval Latin and formulaic discourse, and we discuss some implications of the results in terms of record-keeping practices in High Middle Ages. |
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| AbstractList | This paper presents a model aiming to automatically detect sections in medieval Latin charters. These legal sources are some of the most important sources for medieval studies as they reflect economic and social dynamics as well as legal and institutional writing practices. An automatic linear segmentation can greatly facilitate charter indexation and speed up the recovering of evidence to support historical hypothesis by the means of granular inquiries on these raw, rarely structured sources. Our model is based on a Bi-LSTM approach using a final CRF-layer and was trained using a large, annotated collection of medieval charters (4,700 documents) coming from Lombard monasteries: the CDLM corpus (11th-12th centuries). The evaluation shows a high performance in most sections on the test-set and on an external evaluation corpus consisting of the Montecassino abbey charters (10th-12th centuries). We describe the architecture of the model, the main problems related to the treatment of medieval Latin and formulaic discourse, and we discuss some implications of the results in terms of record-keeping practices in High Middle Ages. |
| Author | Tannier, Xavier Torres Aguilar, Sergio Chastang, Pierre |
| Author_xml | – sequence: 1 givenname: Sergio orcidid: 0000-0002-1801-3147 surname: Torres Aguilar fullname: Torres Aguilar, Sergio organization: École Nationale des Chartes, École nationale des chartes – sequence: 2 givenname: Pierre orcidid: 0000-0001-7181-4835 surname: Chastang fullname: Chastang, Pierre organization: Versailles Saint-Quentin-en-Yvelines University, Université de Versailles Saint-Quentin-en-Yvelines – sequence: 3 givenname: Xavier orcidid: 0000-0002-2452-8868 surname: Tannier fullname: Tannier, Xavier organization: Sorbonne University, Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé |
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| Keywords | digital diplomatics medieval digital studies linear text segmentation Latin NLP medieval charters automatic structure detection |
| Language | English |
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| Snippet | This paper presents a model aiming to automatically detect sections in medieval Latin charters. These legal sources are some of the most important sources for... |
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| SubjectTerms | [info.info-lg]computer science [cs]/machine learning [cs.lg] [info.info-tt]computer science [cs]/document and text processing [shs.hist]humanities and social sciences/history automatic structure detection Computer Science digital diplomatics Document and Text Processing History Humanities and Social Sciences latin nlp linear text segmentation Machine Learning medieval charters medieval digital studies |
| Title | Automatic medieval charters structure detection : A Bi-LSTM linear segmentation approach |
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