Finding a satisfying e-resource that consists of legal documents in Sri Lanka is difficult. On behalf of making good platform to facilitate lawyers and other people who works with legal documents to search and refer legal documents, it is a vital task to extract keywords in the documents. With that aim we did this research to identify keywords in legal articles using machine learning techniques. This paper presents a comprehensive analysis of keyword extraction in legal domain by providing existing analysis , related works are also concerned. Because nowadays there are a lot of legal documents offered in electronic format. Therefore, it is easy for legal scholars and professionals to search and refer details of legal documents. The objective of this analysis is to explore an associate economical way to implement an algorithm to identify keywords in legal articles in Sri Lanka. If manual keyword extraction happens and if there are ten thousand documents to upload the system, and it takes ten minutes to extract core details manually for a particular document. Then for a person who works eight hours per day ,he needs two hundred nine days to complete this task. Thus the system to analyze this legal knowledge will serve effectively for lawyers and law students, which might address a lawyer’s role and may even become powerful to unleash such a task in the future. Designers of such systems face a key challenge that the bulk of those documents are in natural language streams which are lacking formal structure or different specific linguistics information. During this analysis, an associate unsupervised learning approach for automatically distinguishing necessary details in each legal document is extracted. The machine learning and deep learning algorithms based mostly analysis systems apply these strategies in the main for the document classification. Legal document classification, translation, account, data gathering are part of the goals obtained from this research. During this study, we tried to implement two different approaches and compare the success of keyword extraction. This paper is structured as follows. Introduction addressed the problem and some related works on the keyword extraction, then in next sections the methodology of our research and the results evaluation and finally the conclusion of our study is presented.

Index Terms—keyword extraction, Machine Learning, Text Rank,TF-IDF, Legal Articles"