Applying NLP to Support Legal Decision-making in Administrative Appeal Boards in the EU
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Applying NLP to Support Legal Decision-making in Administrative Appeal Boards in the EU. / Olsen, Henrik Palmer; Højmark-Bertelsen, Malte; Schwemer, Sebastian Felix.
In: CEUR Workshop Proceedings, Vol. 3441, 2023, p. 103-110.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Applying NLP to Support Legal Decision-making in Administrative Appeal Boards in the EU
AU - Olsen, Henrik Palmer
AU - Højmark-Bertelsen, Malte
AU - Schwemer, Sebastian Felix
N1 - Publisher Copyright: © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
PY - 2023
Y1 - 2023
N2 - While Natural Language Processing (NLP) is being applied in an increasing number of contexts, including law, it remains a difficult task to leverage NLP for the purpose of real-life support of legal decision-making. This is because 1) legal-decision making must be made in a way that is sensitive not only to legislation but also to evolving case practice (prior decision-making that functions as precedent), 2) legal-decision making is sensitive to open-ended legislative language and shifting factual contexts, 3) traditional methods of NLP are capable of processing long texts, but they are suboptimal compared to novel methods, i.e., transformer-based models, e.g., BERT [1], etc. 4) however the transformer-based models are limited by maximum input lengths, which makes it difficult to apply in real-life scenarios, where legal documents exceed the maximum input length. In this paper, we show how we tackle the problem of providing NLP-based intelligence support to legal decision-makers in a real-world setting using transformer-based NLP.
AB - While Natural Language Processing (NLP) is being applied in an increasing number of contexts, including law, it remains a difficult task to leverage NLP for the purpose of real-life support of legal decision-making. This is because 1) legal-decision making must be made in a way that is sensitive not only to legislation but also to evolving case practice (prior decision-making that functions as precedent), 2) legal-decision making is sensitive to open-ended legislative language and shifting factual contexts, 3) traditional methods of NLP are capable of processing long texts, but they are suboptimal compared to novel methods, i.e., transformer-based models, e.g., BERT [1], etc. 4) however the transformer-based models are limited by maximum input lengths, which makes it difficult to apply in real-life scenarios, where legal documents exceed the maximum input length. In this paper, we show how we tackle the problem of providing NLP-based intelligence support to legal decision-makers in a real-world setting using transformer-based NLP.
KW - automation bias
KW - decision support
KW - legal decision-making
KW - Legal information retrieval
KW - NLP
KW - public administration
M3 - Conference article
AN - SCOPUS:85167803037
VL - 3441
SP - 103
EP - 110
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
SN - 1613-0073
T2 - 6th Workshop on Automated Semantic Analysis of Information in Legal Text, ASAIL 2023
Y2 - 23 September 2023
ER -
ID: 368339915