CFP last date
16 December 2024
Reseach Article

A Question Classification in Closed Domain Question-Answer Systems

by J�ferson N. Soares, Haniel G. Cavalcante, Jos� E.B. Maia
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 38
Year of Publication: 2021
Authors: J�ferson N. Soares, Haniel G. Cavalcante, Jos� E.B. Maia
10.5120/ijais2021451913

J�ferson N. Soares, Haniel G. Cavalcante, Jos� E.B. Maia . A Question Classification in Closed Domain Question-Answer Systems. International Journal of Applied Information Systems. 12, 38 ( July 2021), 1-5. DOI=10.5120/ijais2021451913

@article{ 10.5120/ijais2021451913,
author = { J�ferson N. Soares, Haniel G. Cavalcante, Jos� E.B. Maia },
title = { A Question Classification in Closed Domain Question-Answer Systems },
journal = { International Journal of Applied Information Systems },
issue_date = { July 2021 },
volume = { 12 },
number = { 38 },
month = { July },
year = { 2021 },
issn = { 2249-0868 },
pages = { 1-5 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number38/1120-2021451913/ },
doi = { 10.5120/ijais2021451913 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:11:15.778476+05:30
%A J�ferson N. Soares
%A Haniel G. Cavalcante
%A Jos� E.B. Maia
%T A Question Classification in Closed Domain Question-Answer Systems
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 38
%P 1-5
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A Question-Answer System (QAS) is a Natural Language Processing (NLP) application whose purpose is to answer questions from users by consulting one or more available knowledge bases. Classifying the question posed by a user in a class of a predetermined set has risks and advantages. On the one hand, the correct classification reduces the scope of the search for the answer, generally resulting in more correct answers and greater processing efficiency of the QAS; on the other hand, an error in the classification reduces the chances of the system recovering from this error in the later stages of processing, thus resulting, almost always, in unsatisfactory responses. This work develops and evaluates a question classification scheme for Closed Domain QAS. The experiments showed that the approaches described for defining class and question classification can be used successfully in QAS based on closed collections of documents.

References
  1. Eleni Adamopoulou and Lefteris Moussiades. An overview of chatbot technology. In IFIP International Conference on Artificial Intelligence Applications and Innovations, pages 373–383. Springer, 2020.
  2. Christopher G Atkeson, Andrew W Moore, and Stefan Schaal. Locally weighted learning. Artificial Intelligence Review, 1(11):11–73, 1997.
  3. Steven Bird. Nltk: the natural language toolkit. In Proceedings of the COLING/ACL 2006 Interactive Presentation Sessions, pages 69–72, 2006.
  4. Payal Biswas, Aditi Sharan, and Rakesh Kumar. Question classification using syntactic and rule based approach. In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pages 1033– 1038. IEEE, 2014.
  5. Eduardo G Cortes, Vinicius Woloszyn, and Dante AC Barone. When, where, who, what or why? a hybrid model to question answering systems. In International Conference on Computational Processing of the Portuguese Language, pages 136–146. Springer, 2018.
  6. Fabiano Tavares da Silva and Jos´e EB Maia. Query expansion in text information retrieval with local context and distributional model. J. Digit. Inf. Manag., 17(6):313, 2019.
  7. Fabiano Tavares da Silva and Jose Everardo Bessa Maia. Luppar: Information retrieval for closed text document collections. International Journal of Applied Information Systems, 12(28):1–6, March 2020.
  8. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, 2019.
  9. Hai Doan-Nguyen and Leila Kosseim. Improving the precision of a closed-domain question-answering system with semantic information. In Coupling approaches, coupling media and coupling languages for information retrieval, pages 850–859. LE CENTRE DE HAUTES ETUDES INTERNATIONALES D’INFORMATIQUE DOCUMENTAIRE, 2004.
  10. Ulf Hermjakob. Parsing and question classification for question answering. In Proceedings of the workshop on Open-domain question answering-Volume 12, pages 1–6. Association for Computational Linguistics, 2001.
  11. Jo˜ao Marcos Carvalho Lima and Jos´e Everardo Bessa Maia. A topical word embeddings for text classification. In Anais do XV Encontro Nacional de Inteligˆencia Artificial e Computacional, pages 25–35. SBC, 2018.
  12. Christopher D Manning, Hinrich Sch¨utze, and Prabhakar Raghavan. Introduction to information retrieval. Cambridge university press, 2008.
  13. Alaa Mohasseb, Mohamed Bader-El-Den, and Mihaela Cocea. Question categorization and classification using grammar based approach. Information Processing & Management, 54(6):1228–1243, 2018.
  14. Diego Moll´a and Jos´e Luis Vicedo. Question answering in restricted domains: An overview. Computational Linguistics, 33(1):41–61, 2007.
  15. Fabian Pedregosa, Ga¨el Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine learning in python. the Journal of machine Learning research, 12:2825–2830, 2011.
  16. Xiaojun Quan, Liu Wenyin, and Bite Qiu. Term weighting schemes for question categorization. IEEE trans. on pattern analysis and machine intelligence, 33(5):1009–1021, 2010.
  17. Radim R? ehr?ek, Petr Sojka, et al. Gensimstatistical semantics in python. Retrieved from genism. org, 2011.
  18. Valtemir A Silva, Ig Ibert Bittencourt, and Jos´e C Maldonado. Automatic question classifiers: a systematic review. IEEE Trans. on Learning Technologies, 12(4):485– 502, 2018.
  19. Irfandy Thalib, Indah Soesanti, et al. A review on question analysis, document retrieval and answer extraction method in question answering system. In 2020 International Conference on Smart Technology and Applications (ICoSTA), pages 1–5. IEEE, 2020.
  20. Nguyen Van-Tu and Le Anh-Cuong. Improving question classification by feature extraction and selection. Indian Journal of Science and Technology, 9(17):1–8, 2016.
  21. Andrew R Webb. Statistical pattern recognition. John Wiley & Sons, 2003.
  22. Jerry Wei, Chengyu Huang, Soroush Vosoughi, and Jason Wei. What are people asking about covid-19? a question classification dataset. arXiv preprint arXiv:2005.12522, 2020.
Index Terms

Computer Science
Information Sciences

Keywords

Question Classification Question-Answer System Closed Document Collection Information Retrieval