Advancement of information extraction use in legal documents

Solihin, Firdaus; Budi, Indra; Aji, Rizal Fathoni; Makarim, Edmon

doi:10.1080/13600869.2021.1964225

Cited by 5 publications

(2 citation statements)

References 83 publications

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“…e cyclic neural network is mainly used to process text data of time series. In the research field of natural language processing, such as Chinese word segmentation, text classification, and reading comprehension, recurrent neural networks are often used [17]. eir characteristic is that the input text is closely related to the time series, which is also a symbolic difference from the feedforward neural network.…”

Section: E Neuralmentioning

confidence: 99%

Deep Learning for Decision Element Extraction in Fact Description of Legal Documents

2022

Advances in Multimedia

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In order to improve the work efficiency of judicial personnel and solve the problem of waste of judicial resources, this topic proposes a method of decision element extraction in the fact description of legal documents based on in-depth learning. Firstly, this paper briefly introduces the basic theory of deep learning, text mining technology, a neural network, and other theories and technologies, then expounds the decision element extraction model in the fact description of legal documents based on deep learning, such as the HMM model, the CRF model, and the Bert model, and finally expounds the establishment and implementation method of the decision element extraction model in the fact description of legal documents, so as to provide a guarantee for the work quality and efficiency of judicial personnel. The samples are according to the sample label frequency to obtain more balanced data, and we manually label keywords to obtain feature vectors to assist the model in improving the prediction results, but it also increases the statistical quantity mode of label co-occurrence. Although all modes can be included by using a larger matrix, the amount of calculation increases significantly. Therefore, the follow-up work mainly studies the important co-occurrence features that can be used and then adopts better dimensionality reduction methods to improve the final prediction results.

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Section: E Neuralmentioning

confidence: 99%

Deep Learning for Decision Element Extraction in Fact Description of Legal Documents

2022

Advances in Multimedia

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“…Esta é uma área que é caracterizada por possuir estruturas implícitas e uma linguagem peculiar com características únicas, e que tem despertado muito interesse tanto por suas peculiaridades quanto pelos novos recursos, atualmente, disponíveis para definir novas metodologias de gestão de documentos legais, dentre elas a área de Inteligência Artificial [Martins and Silva 2021]. De fato, nos últimos anos, os modelos de aprendizado de máquina e aprendizado profundo têm atraído ampla atenção para o processamento de documentos legais [Solihin et al 2021]. Por exemplo, os trabalhos de [Noguti et al 2020], , [Silva and Maia 2020] e [Dal Pont et al 2020] mostram a aplicação de modelos de aprendizagem profunda, clássicos e estatísticos para classificação de documentos no contexto jurídico do Brasil.…”

Section: Introductionunclassified

A Textual Representation Based on Bag-of-Concepts and Thesaurus for Legal Information Retrieval

Costa

Pedrosa

2022

Anais Do X Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2022)

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The retrieval of similar textual documents is a challenging task for the legal area due to its peculiar language with unique characteristics. This paper presents a new approach, called BoC-Th, proposed to represent legal documents based on the Bag-of-Concept (BoC) approach, which generates concept through clustering word vectors generated from a basic neural network model, and compute the frequencies of these concept clusters to represent document vectors. The novel contribution of the BoC-Th is to generate weighted histograms of concepts defined from the distance of the word to its respective similar term within a thesaurus. The idea is to emphasize those words that have more significance for the context, thus generating more discriminative vectors. Experimental evaluations were performed by comparing the proposed approach with the traditional BoW and BoC approaches, both popular techniques for document representation. The proposed method obtained the best result among the evaluated techniques for retrieving judgments and jurisprudence documents. The BoC-Th increased the mAP (mean Average Precision) in 51% compared to the traditional BoC approach, while being up to 3.4 times faster than the traditional BoW representation.

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Exploitation and Merge of Information Sources for Public Procurement Improvement

Nai

Sulis

Pasteris

et al. 2023

Communications in Computer and Information Science

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Advancement of information extraction use in legal documents

Cited by 5 publications

References 83 publications

Deep Learning for Decision Element Extraction in Fact Description of Legal Documents

Deep Learning for Decision Element Extraction in Fact Description of Legal Documents

A Textual Representation Based on Bag-of-Concepts and Thesaurus for Legal Information Retrieval

Exploitation and Merge of Information Sources for Public Procurement Improvement

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