Notice of retraction
Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials, Volume 32, Number 8(2) (2020)
Copyright(C) MYU K.K.
pp. 2659-2672
S&M2291 Research Paper of Special Issue
Published: August 20, 2020

Convolutional-neural-network-based Multilabel Text Classification for Automatic Discrimination of Legal Documents [PDF]

Ming Qiu, Yiru Zhang, Tianqi Ma, Qingfeng Wu, and Fanzhu Jin

(Received January 6, 2020; Accepted May 25, 2020)

Keywords: multilabel learning, text classification, word embedding

Law courts spend too much time reading documents and judging the type of legal cases. This problem becomes more serious as a crime can be classified into several categories at the same time. Thus, legal documents need a multilabel classification. We propose a multilabel text classification model based on multilabel text convolutional neural network (MLTCNN). We scan legal documents and convert them to text data using optical character recognition (OCR) with a charge-coupled device (CCD) sensor. Then, we use Jieba, a word segmentation tool of Chinese letters, and TensorFlow VocabularyProcessor to generate vocabularies. Then, the case description after segmenting each word is mapped into a word index in the vocabularies. We use a word index vector as an input to the MLTCNN. Lastly, we adopt multiple sigmoid functions for multiple binary classifications. The result shows our method to be efficient in finding errors and deviations for similar cases among district courts. This study provides a new method to improve the legal service and to enable fairer law enforcement.

Corresponding author: Qingfeng Wu, Fanzhu Jin

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Cite this article
Ming Qiu, Yiru Zhang, Tianqi Ma, Qingfeng Wu, and Fanzhu Jin, Convolutional-neural-network-based Multilabel Text Classification for Automatic Discrimination of Legal Documents, Sens. Mater., Vol. 32, No. 8, 2020, p. 2659-2672.

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