A Semi Supervised Approach for Catchphrase Classification in Legal Text Documents

@article{Bajwa2017ASS,
  title={A Semi Supervised Approach for Catchphrase Classification in Legal Text Documents},
  author={Imran Sarwar Bajwa and Fatim Karim and Muhammad Asif Naeem and Riaz ul Amin},
  journal={J. Comput.},
  year={2017},
  volume={12},
  pages={451-461}
}
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… Expand
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References

SHOWING 1-10 OF 22 REFERENCES
Extractive summarisation of legal texts
TLDR
Results are encouraging as they achieve state-of-the-art accuracy using robust, automatically generated cue phrase information and the utility of the rhetorical annotation scheme as a model of legal discourse, which provides a clear means for structuring summaries and tailoring them to different types of users. Expand
Summarising Legal Texts: Sentential Tense and Argumentative Roles
TLDR
This work uses state-of-the-art NLP techniques to perform the linguistic annotation of a small sample set in order to explore correlations between linguistic features and argumentative roles and focuses here on the predictive capacity of tense and aspect features for a classifier. Expand
LEXA: Towards Automatic Legal Citation Classification
TLDR
This paper created a large training and test corpus from court decision reports in Australia and showed that, within less than a week, it is possible to develop a good quality knowledge base which considerably outperforms a baseline Machine Learning approach. Expand
Combining Different Summarization Techniques for Legal Text
TLDR
A hybrid approach in which a number of different summarization techniques are combined in a rule-based system using manual knowledge acquisition, where human intuition, supported by data, specifies not only attributes and algorithms, but the contexts where these are best used. Expand
A Controlled Natural Language Interface to Class Models
TLDR
The presented approach works as the user inputs the English specification of software requirements and the approach processes input English to extract SBVR vocabulary and generate a SBVR representation in the form of SBVR rules. Expand
TESC: An approach to TExt classification using Semi-supervised Clustering
TLDR
Experiments demonstrate that, in text classification, TESC outperforms Support Vector Machines and back propagation neural network (BPNN), and produces comparable performance to naive Bayes with EM (Expectation Maximization) however with lower computation complexity. Expand
LetSum, an automatic Legal Text Summarizing system
TLDR
LetSum (Legal text Sum- marizer), a prototype system, is described, which determines the thematic structure of a judgment in four themes INTRODUCTION, CONTEXT, JURIDICAL ANALYSIS and CONCLUSION, which identifies the relevant sentences for each theme. Expand
Deep semantic interpretations of legal texts
TLDR
It is shown that a state-of-the-art statistical parser can handle even the complex syntactic constructions of an appellate court judge, and that a deep semantic interpretation of the full text of a judicial opinion can be computed automatically from the output of the parser. Expand
Supervised Machine Learning for Summarizing Legal Documents
TLDR
A supervised machine learning approach for summarizing legal documents and sentence classification experiments relying on a Naive Bayes classifier using a set of surface, emphasis, and content features are presented. Expand
Citation Based Summarisation of Legal Texts
TLDR
This work aims at generating automatically catchphrases for legal case reports, using, beside the full text, also the text of cited cases and cases that cite the current case. Expand
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