A Review on the Application of Deep Learning in Legal Domain

2019 ◽  
pp. 374-381 ◽  
Author(s):  
Neha Bansal ◽  
Arun Sharma ◽  
R. K. Singh
Keyword(s):  
Deep Learning ◽  
Legal Domain

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.

scholarly journals Analysing Predictive Coding Algorithms for Document Review

2021 ◽  
Vol 9 (11) ◽  
pp. 1679-1681
Author(s):  
Aditi Wikhe
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Text Classification ◽  
Unscented Kalman Filter ◽  
Predictive Coding ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Legal Domain ◽  
The Right ◽  
Document Review

Abstract: Lawsuits and regulatory investigations in today's legal environment demand corporations to engage in increasingly intense data-focused engagements to find, acquire, and evaluate vast amounts of data. In recent years, technology-assisted review (TAR) has become a more crucial part of the document review process in legal discovery. Attorneys now have been using machine learning techniques like text classification to identify responsive information. In the legal domain, text classification is referred to as predictive coding or technology assisted review (TAR). Predictive coding is used to increase the number of relevant documents identified, while reducing human labelling efforts and manual review of documents. Deep learning models mixed with word embeddings have demonstrated to be more effective in predictive coding in recent years. Deep learning models, on the other hand, have a lot of variables, making it difficult and time-consuming for legal professionals to choose the right settings. In this paper, we will look at a few predictive coding algorithms and discuss which one is the most efficient among them. Keywords: Technology-assisted-review, predictive coding, machine learning, text classification, deep learning, CNN , Unscented Kalman Filter, Logistic Regression, SVM

Sigmalaw PBSA - A Deep Learning Model for Aspect-Based Sentiment Analysis for the Legal Domain

2021 ◽  
pp. 125-137
Author(s):  
Isanka Rajapaksha ◽  
Chanika Ruchini Mudalige ◽  
Dilini Karunarathna ◽  
Nisansa de Silva ◽  
Amal Shehan Perera ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Learning Model ◽  
Legal Domain ◽  
Deep Learning Model

scholarly journals An Artificial Intelligence based Analysis in Legal domain

2019 ◽  
Vol 9 (2S2) ◽  
pp. 1046-1051
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Recurrent Network ◽  
Law Students ◽  
Legal Information ◽  
Legal Perspective ◽  
Legal Document ◽  
Legal Domain ◽  
Gated Recurrent Unit ◽  
Unit Network

Around the world, legitimate information and common laws are available in raw form, but hard to understand and not in organized form. All legitimate information is nowadays computerized since the legal information gets generated on a regular basis in a huge volume due to increase of maritime (law) courts. The automation tool to analyse this legal data can serve effectively for lawyers and law students, which can address a lawyer’s role and can even become powerful to release such a role in future. The machine learning and deep learning algorithmsbased analysis systems apply these methods mainly for document classification. Legal document translation, text classification, summarization, data forecasting and data obtainment are part of the goals got from research charity. In this study, we review about the different methods of deep learning used in legal tasks such as Legal data search, Legal document analytics, and Legal perspective interface. To solve aggregate tasks, one can use the deep learning methods like, Recurrent Network Networks (RNN), Gated Recurrent unit network (GRU), Long Short Term Memory networks (LSTM), Convolutional neural network (CNN). Through this review, we instituted that deep learning models are giving advanced performance

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

Vollautomatische Quantifizierung der Volumina und Funktion des linken Ventrikels in der kardialen MRT: Evaluation eines Deep-Learning basierten Algorithmus

2020 ◽  
Author(s):  
B Böttcher ◽  
E Beller ◽  
A Busse ◽  
F Streckenbach ◽  
M Weber ◽  
...  
Keyword(s):  
Deep Learning
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