A comparative study of automated legal text classification using random forests and deep learning

2022 ◽  
Vol 59 (2) ◽  
pp. 102798
Author(s):  
Haihua Chen ◽  
Lei Wu ◽  
Jiangping Chen ◽  
Wei Lu ◽  
Junhua Ding
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Random Forests ◽  
Text Classification ◽  
Legal Text

Comparative Study on Telugu text Classification using Machine Learning and Deep Learning models

2021 ◽  
Author(s):  
Veerraju Gampala ◽  
Jaideep Vallapuneni ◽  
Pavan Kumar Ande ◽  
Ravindra Kumar Indurthi ◽  
Nichenametla Rajesh
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Comparative Study ◽  
Text Classification ◽  
Learning Models

scholarly journals The Unreasonable Effectiveness of the Baseline: Discussing SVMs in Legal Text Classification

2021 ◽  
Author(s):  
Benjamin Clavié ◽  
Marc Alphonsus
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Text Classification ◽  
Traditional Approach ◽  
Error Reduction ◽  
Support Vector ◽  
Learning Models ◽  
Legal Text ◽  
Classification Tasks ◽  
Legal Domain

We aim to highlight an interesting trend to contribute to the ongoing debate around advances within legal Natural Language Processing. Recently, the focus for most legal text classification tasks has shifted towards large pre-trained deep learning models such as BERT. In this paper, we show that a more traditional approach based on Support Vector Machine classifiers reaches competitive performance with deep learning models. We also highlight that error reduction obtained by using specialised BERT-based models over baselines is noticeably smaller in the legal domain when compared to general language tasks. We discuss some hypotheses for these results to support future discussions.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

On the cost-effectiveness of neural and non-neural approaches and representations for text classification: A comprehensive comparative study

2021 ◽  
Vol 58 (3) ◽  
pp. 102481
Author(s):  
Washington Cunha ◽  
Vítor Mangaravite ◽  
Christian Gomes ◽  
Sérgio Canuto ◽  
Elaine Resende ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Comparative Study ◽  
Text Classification ◽  
The Cost
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