Comparative Analysis of Different Argumentation Frameworks


  • Shashi Prabha Anan, Vaishali Singh


Natural Language Processing, Argumentation Mining, Structured and Unstructured Data sets, Artificial Intelligence, Computational Argumentation.


Argumentation Mining is considered a much harder task than generic information extraction or event mining because argumentation structures can be nested recursively. That is, a complete argumentation structure (claim and premises) might function as the premise of some more general claim, and so on. Recognizing the relationships among components of an argument also requires real-world knowledge, including knowing when one thing is a subtype of another. Both use NLP methods to map unstructured text onto graph-like structures or databases. The resulting information is easier to analyze for a variety of tasks, such as learning about social or political views, advising people about how to weigh the evidence for or against some choice, or helping companies to market products or perform quality assurance. Most of these tasks use hand-built templates that have been specified to fit a particular task or observed style of communication.


Download data is not yet available.


Sara Rosenthal and Kathleen McKeown. 2012. Detecting Opinionated Claims in Online Discussions. In Sixth IEEE International Conference on Semantic Computing, ICSC 2012, Palermo, Italy, September 19-21, 2012. IEEE Computer Society, 30–37.

C.M. de Farias, L. Pirmez, F.C. Delicato, W. Li, A.Y. Zomaya, J.N. de Souza, A scheduling algorithm for shared sensor and actuator networks, Int. Conf. Inf. Netw. 2013. (2013) 648-653. doi:10.1109/ICOIN.2013.6496703.

D. Zeng, L. Gu, S. Guo, Z. Cheng, S. Yu, Joint Optimization of Task Scheduling and Image Placement in Fog Computing Supported Software-Defined Embedded System, IEEE Trans. Comput. PP (2016) 1-1.doi:10.1109/TC.2016.2536019.

S. Kosakovsky Pond, “Computational analysis of HIV-1 evolution and epidemiology,” Bioinformaics and Biomedicine Workshops (BIBMW), 2011 IEEE International Conference, pp 60-63, Nov 2011.

Andreas Peldszus and Manfred Stede. 2013. From Argument Diagrams to Argumentation Mining in Texts: A Survey. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 7, 1 (2013),

Sinno Jialin Pan and Qiang Yang. 2010. A Survey on Transfer Learning. Knowledge and Data Engineering, IEEE Transactions on 22, 10 (Oct 2010), 1345–1359.

Hoifung Poon and Pedro Domingos. 2007. Joint Inference in Information Extraction. In Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 2007, Vancouver, Canada. AAAI Press, 913–918.

O.S. Balogun, “Evaluation of logistic regression on mode of the delivery of expectant mothers,” International Journal of Bioassay, vol 4 no 6,pp 3900-3993, 2015.

S. Kalmegh, “Analysis of WEKA data mining algorithm REPTree, Simple Cart and RandomTree for classification of Indian News,” International Journal of Innovative Science, Engineering & Technology, vol 2 no 2, pp 438-446, 2015.

S. Kosakovsky Pond, “Computational analysis of HIV-1 evolution and epidemiology,” Bioinformaics and Biomedicine Workshops (BIBMW), 2011 IEEE International Conference, pp 60-63, Nov 2011.

O.S. Balogun, T.J. Akingbade, and A.A. Akinrefon, “Evaluation of logistic regression in classification of drug data in Kwara State,” International Journal of Computational Engineering Research, vol 3 no 3, pp 54-58, 2013.




How to Cite

Shashi Prabha Anan, Vaishali Singh. (2024). Comparative Analysis of Different Argumentation Frameworks . International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 776–779. Retrieved from



Research Article