Overview of SMP-CAIL2020-Argmine: The Interactive Argument-Pair
                    Extraction in Judgement Document Challenge

Yuan, Jian; Wei, Zhongyu; Gao, Yixu; Chen, Wei; Song, Yun S.; Zhao, Deli; Ma, Jinglei; Hu, Zhen; Zou, Shaokun; Li, Donghai; Huang, Xuanjing

doi:10.1162/dint_a_00094

Cited by 6 publications

(1 citation statement)

References 28 publications

(21 reference statements)

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“…The CAIL2020 abstract dataset (Yuan J, 2021) [21] used in the experiment totaled 9848 civil rst instance judgments. The judgment document is divided into several sentences in advance, each sentence is marked with the label of whether it is important, and the corresponding full text of the reference abstract is provided.…”

Section: Experimental Datamentioning

confidence: 99%

Design of intelligent legal text analysis and information retrieval system based on BERT model

Li,

Wang

2023

Preprint

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The amalgamation of information retrieval systems and soft computing techniques establishes a robust framework to confront the challenges and seize the opportunities presented by the vast expanse of big data. As the volume, diversity, and velocity of data continue to proliferate, further advancements in this domain are poised to significantly contribute to various realms, encompassing healthcare, finance, e-commerce, and scientific research, ultimately propelling innovation and facilitating decision-making in the epoch of big data. The application of artificial intelligence (AI) technology to the analysis and comprehension of legal documents holds the potential to expedite the acquisition of case-specific information by legal researchers, thereby expediting their research endeavors. This paper proposes a legal text analysis and retrieval model, rooted in the Bleem model (Bert-based Legal Paper's Element Extracting Model). Initially, our model employs Bert as the coding layer to extract the semantic information embedded within document sentences and element exemplars. Subsequently, we leverage the Attention mechanism to align the semantic essence of element example sentences with document sentences, while simultaneously computing their respective attention weights. Comparative experiments and attention visualization are then employed to validate the efficacy of the Bleem model. The experimental results corroborate the superiority of the Bleem model in terms of accuracy and F1 scores. The visualization of the attention mechanism effectively reveals the inner workings of the Bleem model and unveils its capacity to explore pertinent fragments within document sentences and element examples. Building upon the aforementioned model, we have devised an intelligent legal text analysis and retrieval system, empowering legal researchers to swiftly acquire pivotal data through case briefs. This application has effectively fostered the integration of legal services within the holistic management of public risks.

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Section: Experimental Datamentioning

confidence: 99%