2022
DOI: 10.1007/s10506-022-09341-8
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Attentive deep neural networks for legal document retrieval

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Cited by 13 publications
(1 citation statement)
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“…Given an input legal question, the former helps to narrow down and locates a subset of most relevant documents while the latter attempts to give a yes/no answer to the question by analyzing those relevant documents. Legal document retrieval and textual entailment have several challenges, such as intricate legal language structures (Nguyen et al, 2022b), scarcity of annotated legal data (Nguyen et al, 2022a;Yoshioka et al, 2021), rich vocabulary with multiple meanings (Nguyen et al, 2021), and high dependency and the interrelationship between legal statutes (Vuong et al, 2022).…”
Section: Introductionmentioning
confidence: 99%
“…Given an input legal question, the former helps to narrow down and locates a subset of most relevant documents while the latter attempts to give a yes/no answer to the question by analyzing those relevant documents. Legal document retrieval and textual entailment have several challenges, such as intricate legal language structures (Nguyen et al, 2022b), scarcity of annotated legal data (Nguyen et al, 2022a;Yoshioka et al, 2021), rich vocabulary with multiple meanings (Nguyen et al, 2021), and high dependency and the interrelationship between legal statutes (Vuong et al, 2022).…”
Section: Introductionmentioning
confidence: 99%