A significant concern in processing natural language data is the often unclear legal status of the input and output data/resources. In this paper, we investigate this problem by discussing a typical activity in Natural Language Processing: the training of a machine learning model from an annotated corpus. We examine which legal rules apply at relevant steps and how they affect the legal status of the results, especially in terms of copyright and copyright-related rights.

A Legal Perspective on Training Models for Natural Language Processing / Eckart De Castilho, Richard; Dore, Giulia; Margoni, Thomas; Labropoulou, Penny; Gurevych, Iryna. - ELETTRONICO. - (2018), pp. 1267-1274. ( 11th edition of the Language Resources and Evaluation Conference (LREC) Miyazaki, Japan 7th-12th May 2018).

A Legal Perspective on Training Models for Natural Language Processing

Dore, Giulia
Primo
;
Margoni, Thomas
Ultimo
;
2018-01-01

Abstract

A significant concern in processing natural language data is the often unclear legal status of the input and output data/resources. In this paper, we investigate this problem by discussing a typical activity in Natural Language Processing: the training of a machine learning model from an annotated corpus. We examine which legal rules apply at relevant steps and how they affect the legal status of the results, especially in terms of copyright and copyright-related rights.
2018
LREC 2018: Eleventh International Conference on Language Resources and Evaluation
Paris
European Language Resources Association (ELRA)
979-10-95546-00-9
Eckart De Castilho, Richard; Dore, Giulia; Margoni, Thomas; Labropoulou, Penny; Gurevych, Iryna
A Legal Perspective on Training Models for Natural Language Processing / Eckart De Castilho, Richard; Dore, Giulia; Margoni, Thomas; Labropoulou, Penny; Gurevych, Iryna. - ELETTRONICO. - (2018), pp. 1267-1274. ( 11th edition of the Language Resources and Evaluation Conference (LREC) Miyazaki, Japan 7th-12th May 2018).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/203062
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