Extracting and classifying exceptional COVID-19 measures from multilingual legal texts: The merits and limitations of automated approaches

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A7GQ6Q7FE" target="_blank" >RIV/00216208:11320/23:7GQ6Q7FE - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173470159&doi=10.1111%2frego.12557&partnerID=40&md5=8d6498accbc87a59b539b94869c3c4dd" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173470159&doi=10.1111%2frego.12557&partnerID=40&md5=8d6498accbc87a59b539b94869c3c4dd</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/rego.12557" target="_blank" >10.1111/rego.12557</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Extracting and classifying exceptional COVID-19 measures from multilingual legal texts: The merits and limitations of automated approaches

Popis výsledku v původním jazyce

"This paper contributes to ongoing scholarly debates on the merits and limitations of computational legal text analysis by reflecting on the results of a research project documenting exceptional COVID-19 management measures in Europe. The variety of exceptional measures adopted in countries characterized by different legal systems and natural languages, as well as the rapid evolution of such measures, pose considerable challenges to manual textual analysis methods traditionally used in the social sciences. To address these challenges, we develop a supervised classifier to support the manual coding of exceptional policies by a multinational team of human coders. After presenting the results of various natural language processing (NLP) experiments, we show that human-in-the-loop approaches to computational text analysis outperform unsupervised approaches in accurately extracting policy events from legal texts. We draw lessons from our experience to ensure the successful integration of NLP methods into social science research agendas. © 2023 The Authors. Regulation & Governance published by John Wiley & Sons Australia, Ltd."

Název v anglickém jazyce

Extracting and classifying exceptional COVID-19 measures from multilingual legal texts: The merits and limitations of automated approaches

Popis výsledku anglicky

"This paper contributes to ongoing scholarly debates on the merits and limitations of computational legal text analysis by reflecting on the results of a research project documenting exceptional COVID-19 management measures in Europe. The variety of exceptional measures adopted in countries characterized by different legal systems and natural languages, as well as the rapid evolution of such measures, pose considerable challenges to manual textual analysis methods traditionally used in the social sciences. To address these challenges, we develop a supervised classifier to support the manual coding of exceptional policies by a multinational team of human coders. After presenting the results of various natural language processing (NLP) experiments, we show that human-in-the-loop approaches to computational text analysis outperform unsupervised approaches in accurately extracting policy events from legal texts. We draw lessons from our experience to ensure the successful integration of NLP methods into social science research agendas. © 2023 The Authors. Regulation & Governance published by John Wiley & Sons Australia, Ltd."

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

"Regulation and Governance"

ISSN

1748-5983

e-ISSN

—

Svazek periodika

""

Číslo periodika v rámci svazku

2023

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

20

Strana od-do

1-20

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85173470159