Return of the AI: An analysis of legal research on Artificial Intelligence using topic modeling

Constanta Rosca; Bogdan Covrig; Catalina Goanta; Gijs van Dijck; Gerasimos Spanakis

Return of the AI: An analysis of legal research on Artificial Intelligence using topic modeling

Constanta Rosca, Bogdan Covrig, Catalina Goanta, Gijs van Dijck, Gerasimos Spanakis

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

555 Downloads (Pure)

Abstract

AI research finds itself in the third boom of its history, and in recent years, AI-related themes have gained considerable popularity in new disciplines, such as law. This paper explores what legal research on AI constitutes of and how it has evolved, while addressing the issues of information retrieval and research duplication. Using Latent Dirichlet Allocation (LDA) topic modeling on a dataset of 3931 journal articles, we explore three questions: (a) Which topics within legal research on AI can be distinguished? (b) When were these topics addressed? and (c) Can similar papers be detected? The topic modeling results in a total of 32 meaningful topics. Additionally, it is found that legal research on AI drastically increased as of 2016, with topics becoming more granular and diverse over time. Finally, a comparison of the similarity assessments produced by the algorithm and a human expert suggest that the assessments often coincide. The results provide insights into how a legal research on AI has evolved over time, and support for the development of machine learning and information retrieval tools like LDA that assist in structuring large document collections and identifying relevant articles.

Original language	English
Title of host publication	Proceedings of the Natural Legal Language Processing Workshop 2020
Editors	Nikolaos Aletras, Ion Androutsopoulos, Leslie Barrett, Adam Meyers, Daniel Preoţiuc-Pietro
Publisher	CEUR-WS.org
Pages	3-10
Number of pages	8
Publication status	Published - 24 Aug 2020

Publication series

Series	CEUR Workshop Proceedings
Volume	2645
ISSN	1613-0073

Access to Document

NLLP_Return of the AI_July2020Final published version, 612 KB

http://ceur-ws.org/Vol-2645/paper1.pdfLicence: CC BY

Cite this

Rosca, C., Covrig, B., Goanta, C., van Dijck, G., & Spanakis, G. (2020). Return of the AI: An analysis of legal research on Artificial Intelligence using topic modeling. In N. Aletras, I. Androutsopoulos, L. Barrett, A. Meyers, & D. Preoţiuc-Pietro (Eds.), Proceedings of the Natural Legal Language Processing Workshop 2020 (pp. 3-10). CEUR-WS.org. http://ceur-ws.org/Vol-2645/paper1.pdf

Rosca, Constanta ; Covrig, Bogdan ; Goanta, Catalina et al. / Return of the AI: An analysis of legal research on Artificial Intelligence using topic modeling. Proceedings of the Natural Legal Language Processing Workshop 2020. editor / Nikolaos Aletras ; Ion Androutsopoulos ; Leslie Barrett ; Adam Meyers ; Daniel Preoţiuc-Pietro. CEUR-WS.org, 2020. pp. 3-10 (CEUR Workshop Proceedings, Vol. 2645).

@inproceedings{7034226bc12c4e53a4fe8f7a5eea4ef2,

title = "Return of the AI: An analysis of legal research on Artificial Intelligence using topic modeling",

abstract = "AI research finds itself in the third boom of its history, and in recent years, AI-related themes have gained considerable popularity in new disciplines, such as law. This paper explores what legal research on AI constitutes of and how it has evolved, while addressing the issues of information retrieval and research duplication. Using Latent Dirichlet Allocation (LDA) topic modeling on a dataset of 3931 journal articles, we explore three questions: (a) Which topics within legal research on AI can be distinguished? (b) When were these topics addressed? and (c) Can similar papers be detected? The topic modeling results in a total of 32 meaningful topics. Additionally, it is found that legal research on AI drastically increased as of 2016, with topics becoming more granular and diverse over time. Finally, a comparison of the similarity assessments produced by the algorithm and a human expert suggest that the assessments often coincide. The results provide insights into how a legal research on AI has evolved over time, and support for the development of machine learning and information retrieval tools like LDA that assist in structuring large document collections and identifying relevant articles.",

author = "Constanta Rosca and Bogdan Covrig and Catalina Goanta and {van Dijck}, Gijs and Gerasimos Spanakis",

year = "2020",

month = aug,

day = "24",

language = "English",

series = "CEUR Workshop Proceedings",

publisher = "CEUR-WS.org",

pages = "3--10",

editor = "Aletras, {Nikolaos } and Ion Androutsopoulos and Leslie Barrett and Adam Meyers and Daniel Preo{\c t}iuc-Pietro",

booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2020",

address = "Germany",

}

Rosca, C, Covrig, B, Goanta, C , van Dijck, G & Spanakis, G 2020, Return of the AI: An analysis of legal research on Artificial Intelligence using topic modeling. in N Aletras, I Androutsopoulos, L Barrett, A Meyers & D Preoţiuc-Pietro (eds), Proceedings of the Natural Legal Language Processing Workshop 2020. CEUR-WS.org, CEUR Workshop Proceedings, vol. 2645, pp. 3-10. <http://ceur-ws.org/Vol-2645/paper1.pdf>

Return of the AI: An analysis of legal research on Artificial Intelligence using topic modeling. / Rosca, Constanta; Covrig, Bogdan; Goanta, Catalina et al.
Proceedings of the Natural Legal Language Processing Workshop 2020. ed. / Nikolaos Aletras; Ion Androutsopoulos; Leslie Barrett; Adam Meyers; Daniel Preoţiuc-Pietro. CEUR-WS.org, 2020. p. 3-10 (CEUR Workshop Proceedings, Vol. 2645).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

TY - GEN

T1 - Return of the AI: An analysis of legal research on Artificial Intelligence using topic modeling

AU - Rosca, Constanta

AU - Covrig, Bogdan

AU - Goanta, Catalina

AU - van Dijck, Gijs

AU - Spanakis, Gerasimos

PY - 2020/8/24

Y1 - 2020/8/24

N2 - AI research finds itself in the third boom of its history, and in recent years, AI-related themes have gained considerable popularity in new disciplines, such as law. This paper explores what legal research on AI constitutes of and how it has evolved, while addressing the issues of information retrieval and research duplication. Using Latent Dirichlet Allocation (LDA) topic modeling on a dataset of 3931 journal articles, we explore three questions: (a) Which topics within legal research on AI can be distinguished? (b) When were these topics addressed? and (c) Can similar papers be detected? The topic modeling results in a total of 32 meaningful topics. Additionally, it is found that legal research on AI drastically increased as of 2016, with topics becoming more granular and diverse over time. Finally, a comparison of the similarity assessments produced by the algorithm and a human expert suggest that the assessments often coincide. The results provide insights into how a legal research on AI has evolved over time, and support for the development of machine learning and information retrieval tools like LDA that assist in structuring large document collections and identifying relevant articles.

AB - AI research finds itself in the third boom of its history, and in recent years, AI-related themes have gained considerable popularity in new disciplines, such as law. This paper explores what legal research on AI constitutes of and how it has evolved, while addressing the issues of information retrieval and research duplication. Using Latent Dirichlet Allocation (LDA) topic modeling on a dataset of 3931 journal articles, we explore three questions: (a) Which topics within legal research on AI can be distinguished? (b) When were these topics addressed? and (c) Can similar papers be detected? The topic modeling results in a total of 32 meaningful topics. Additionally, it is found that legal research on AI drastically increased as of 2016, with topics becoming more granular and diverse over time. Finally, a comparison of the similarity assessments produced by the algorithm and a human expert suggest that the assessments often coincide. The results provide insights into how a legal research on AI has evolved over time, and support for the development of machine learning and information retrieval tools like LDA that assist in structuring large document collections and identifying relevant articles.

M3 - Conference article in proceeding

T3 - CEUR Workshop Proceedings

SP - 3

EP - 10

BT - Proceedings of the Natural Legal Language Processing Workshop 2020

A2 - Aletras, Nikolaos

A2 - Androutsopoulos, Ion

A2 - Barrett, Leslie

A2 - Meyers, Adam

A2 - Preoţiuc-Pietro, Daniel

PB - CEUR-WS.org

ER -

Rosca C, Covrig B, Goanta C , van Dijck G , Spanakis G. Return of the AI: An analysis of legal research on Artificial Intelligence using topic modeling. In Aletras N, Androutsopoulos I, Barrett L, Meyers A, Preoţiuc-Pietro D, editors, Proceedings of the Natural Legal Language Processing Workshop 2020. CEUR-WS.org. 2020. p. 3-10. (CEUR Workshop Proceedings, Vol. 2645).