A hybrid model of complexity estimation: Evidence from Russian legal texts
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AXWWWF9QC" target="_blank" >RIV/00216208:11320/22:XWWWF9QC - isvavai.cz</a>
Result on the web
<a href="https://www.frontiersin.org/articles/10.3389/frai.2022.1008530" target="_blank" >https://www.frontiersin.org/articles/10.3389/frai.2022.1008530</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3389/frai.2022.1008530" target="_blank" >10.3389/frai.2022.1008530</a>
Alternative languages
Result language
angličtina
Original language name
A hybrid model of complexity estimation: Evidence from Russian legal texts
Original language description
This article proposes a hybrid model for the estimation of the complexity of legal documents in Russian. The model consists of two main modules: linguistic feature extractor and a transformer-based neural encoder. The set of linguistic metrics includes both non-specific metrics traditionally used to predict complexity, as well as style-specific metrics developed in order to deal with the peculiarities of official texts. The model was trained on a dataset constructed from text sequences from Russian textbooks. Training data were collected on either subjects related to the topic of legal documents such as Jurisprudence, Economics, Social Sciences, or subjects characterized by the use of general languages such as Literature, History, and Culturology. The final set of materials used contain 48 thousand selected text blocks having various subjects and level-of-complexity identifiers. We have tested the baseline fine-tuned BERT model, models trained on linguistic features, and models trained on features in combination with BERT predictions. The scores show that a hybrid approach to complexity estimation can provide high-quality results in terms of different metrics. The model has been tested on three sets of legal documents.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Frontiers in Artificial Intelligence
ISSN
2624-8212
e-ISSN
1744-4217
Volume of the periodical
5
Issue of the periodical within the volume
2022
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
1-14
UT code for WoS article
000913515000001
EID of the result in the Scopus database
2-s2.0-85142114864