KoRASA: Pipeline Optimization for Open-Source Korean Natural Language Understanding Framework Based on Deep Learning

Hwang, Myeong-Ha; Shin, Jikang; Seo, Ho-Jin; Im, Jeong-Seon; Cho, Hee

doi:10.1155/2021/9987462

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…Hwang presented a CNN-based framework that incorporates short text representations for classification. ese representations are combined with regular conceptualized words and related concepts in a knowledge base on top of pretrained word vectors [11].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Translation of Japanese Literature Language and Natural Language Environment Understanding Based on Artificial Neural Network

2022

Journal of Environmental and Public Health

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Literary translation encompasses not only the communication of ideas across cultures but also the conversion of one word into another. The translation of Japanese literature will, therefore, undoubtedly convey this foreign culture. This study examines the semantic expression of words, the NN model for common sense reasoning, the method of building a common sense knowledge base, and the translation of Japanese literary language with an emphasis on the semantic expression and reasoning methods of natural language based on NN. The network structure, parameter processing, learning algorithm, and learning samples are all discussed in detail at the same time as the integrated NN model is presented. According to simulation results, this algorithm can achieve an accuracy of about 95% and a recall rate of about 96.5%. In addition to greatly enhancing the model’s flexibility and popularity, this approach successfully addresses the issue of information loss. It can offer some references for both the translation of literary language and the understanding of natural language.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Translation of Japanese Literature Language and Natural Language Environment Understanding Based on Artificial Neural Network

2022

Journal of Environmental and Public Health

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“…The accuracy of the classification got 96.7% based on Eqs. ( 11)- (13). It is considered higher and authentic accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…The optimizations have been described in different research and have shown the benefit of several optimization designs such as pipeline applications. In [13], the authors described DL with pipeline optimization for the Korean language framework. The paper showed that the entity extraction and the classification were based on the F1-score.…”

Section: Literature Reviewmentioning

confidence: 99%

Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

Aly¹,

Hussein²,

Rahouma³

2022

Computers, Materials &Amp; Continua

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This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees' performance. The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance. The work of this paper consists of two parts. The first part is based on collecting data of employees to calculate and illustrate the performance of each employee. The second part is based on the classification and prediction techniques of the employee performance. This model is designed to help companies in their decisions about the employees' performance. The classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the features. Results of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming years. Results also show that the Grasshopper Optimization, followed by "KF" with the Gradient Boosting Tree as classifier and predictor, is characterized by a high accuracy. The proposed algorithm is compared with other known techniques where our results are fund to be superior.

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“…3.2.1.1 Korean legal data. Hwang et al (2022) introduced the first large-scale benchmark dataset for the Korean legal AI domain. This data set comprises a legal domain corpus, two classification tasks, two legal judgment prediction (LJP) tasks and a summary task.…”

Section: Low-rank Adaptationmentioning

confidence: 99%

Large language models for automated Q&A involving legal documents: a survey on algorithms, frameworks and applications

Yang,

Wang,

Wang

et al. 2024

IJWIS

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Purpose This study aims to adopt a systematic review approach to examine the existing literature on law and LLMs.It involves analyzing and synthesizing relevant research papers, reports and scholarly articles that discuss the use of LLMs in the legal domain. The review encompasses various aspects, including an analysis of LLMs, legal natural language processing (NLP), model tuning techniques, data processing strategies and frameworks for addressing the challenges associated with legal question-and-answer (Q&A) systems. Additionally, the study explores potential applications and services that can benefit from the integration of LLMs in the field of intelligent justice. Design/methodology/approach This paper surveys the state-of-the-art research on law LLMs and their application in the field of intelligent justice. The study aims to identify the challenges associated with developing Q&A systems based on LLMs and explores potential directions for future research and development. The ultimate goal is to contribute to the advancement of intelligent justice by effectively leveraging LLMs. Findings To effectively apply a law LLM, systematic research on LLM, legal NLP and model adjustment technology is required. Originality/value This study contributes to the field of intelligent justice by providing a comprehensive review of the current state of research on law LLMs.

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KoRASA: Pipeline Optimization for Open-Source Korean Natural Language Understanding Framework Based on Deep Learning

Cited by 9 publications

References 18 publications

[Retracted] Translation of Japanese Literature Language and Natural Language Environment Understanding Based on Artificial Neural Network

[Retracted] Translation of Japanese Literature Language and Natural Language Environment Understanding Based on Artificial Neural Network

Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

Large language models for automated Q&A involving legal documents: a survey on algorithms, frameworks and applications

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