Volume 12 Number 5 (Sep. 2017)
Home > Archive > 2017 > Volume 12 Number 5 (Sep. 2017) >
JCP 2017 Vol.12(5): 451-461 ISSN: 1796-203X
doi: 10.17706/jcp.12.5.451-461

A Semi Supervised Approach for Catchphrase Classification in Legal Text Documents

Imran Sarwar Bajwa1, Fatim Karim1, M. Asif Naeem2, Riaz Ul Amin3
1Department of Computer Science & IT, The Islamia University of Bahawalpur.
2School of Computer and Mathematical Sciences, Auckland University of Technology, New Zealand.
3School of Computing Science, University of Glasgow, UK.

Abstract—An agreement between a user and also the owner of a software program known as software license that allows a user to try to certain things that will somewhat be an infringement of copyright law. Typically, a software license agreement is based on set of rules that a user has to comply with while using the software. Sometimes, the price of the software and licensing fees is usually described elsewhere, but also discussed in the licensing agreement, however, typically used catchphrases in software License Agreement make the text of the agreement complex for a common user. Most of the users are very keen to understand the agreement before purchasing software, and it's very necessary for users and business managers to understand these agreements due to further use this software, because these software’s are high cost and risky. In this paper, a semi supervised approach is presented to extract catchphrases from software license agreement and provide meanings to minimize the understanding complexity for a reader. The results of experiments show that the proposed approach for automatic extraction of catchphrases from software license agreements make its interpretation easy and straightforward.

Index Terms—Catchphrases, legal text, natural language processing, software license.


Cite: Imran Sarwar Bajwa, Fatim Karim, M. Asif Naeem, Riaz Ul Amin, "A Semi Supervised Approach for Catchphrase Classification in Legal Text Documents," Journal of Computers vol. 12, no. 5, pp. 451-461, 2017.

General Information

ISSN: 1796-203X
Frequency: Monthly (2006-2014); Bimonthly (Since 2015)
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO, DOAJ, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
  • Sep 26, 2017 News!

    Papers published in JCP Volume 12 have all been indexed by DBLP   [Click]

  • Sep 02, 2016 News!

    Vol 11, No 3 has been indexed by EI (Inspec)   [Click]

  • Sep 22, 2017 News!

    Vol 13, No 6 has been published with online version 11 papers are published in this issue after peer review   [Click]

  • Aug 14, 2017 News!

    Vol 13, No 5 has been published with online version   [Click]

  • Jun 21, 2017 News!

    Vol 13, No 4 has been published with online version   [Click]

  • Read more>>