Comparative Analysis of Different Argumentation Frameworks
Keywords:
Natural Language Processing, Argumentation Mining, Structured and Unstructured Data sets, Artificial Intelligence, Computational Argumentation.Abstract
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.
Downloads
References
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.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
