Analysis of Multinomial Classification for Legal Document Categorization

Title

Analysis of Multinomial Classification for Legal Document Categorization

Creator

Joseph T.; Adiyillam V.

Description

A major area of research today is the application of Machine Learning Techniques for Document or Text Classification. Document Classification is an important aspect of Electronic Discovery in the Legal domain. The need for the process to be automated has been realized over the past few years. Multinomial Classification is a well-known Supervised Machine Learning Technique that helps us classify if there are more than two classes used for the purpose of Classification. Evaluation metrics such as Precision, Recall, and F1 Score have been used to measure the efficiency of Classification. Logistic Regression and Gradient Boosting Algorithms have outperformed other Multiclass Classification techniques. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Source

Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 177-183.

Date

2024-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Legal document classification; Machine learning; Natural language processing

Coverage

Joseph T., Research Scholar, Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, 560029, India; Adiyillam V., Department of Statistics and Data Science, CHRIST (Deemed to Be University), Bengaluru, 560029, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981970974-8

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-97-0975-5_15

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197313171&doi=10.1007%2f978-981-97-0975-5_15&partnerID=40&md5=74b587d74622113ec5714198daf0c3f7