Detecting question intention using a K-Nearest neighbor based approach

Alaa Mohasseb; Mohamed Bader-El-Den; Mihaela Cocea

doi:10.1007/978-3-319-92016-0_10

Detecting question intention using a K-Nearest neighbor based approach

Alaa Mohasseb, Mohamed Bader-El-Den, Mihaela Cocea

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

The usage of question answering systems is increasing daily. People constantly use question answering systems in order to find the right answer for different kinds of information, but the abundance of available data has made the process of obtaining relevant information challenging in terms of processing and analyzing it. Many questions classification techniques have been proposed with the aim of helping in understanding the actual intent of the user’s question. In this research, we have categorized different question types through introducing question type syntactical patterns for detecting question intention. In addition, a k-nearest neighbor based approach has been developed for question classification. Experiments show that our approach has a good level of accuracy in identifying different question types.

Original language	English
Title of host publication	Artificial Intelligence Applications and Innovations
Subtitle of host publication	AIAI 2018 IFIP WG 12.5 International Workshops, SEDSEAL, 5G-PINE, MHDW, and HEALTHIOT, Rhodes, Greece, May 25-27, 2018, Proceedings
Editors	Lazaros Iliadis, Ilias Maglogiannis, Vassilis Plagianakos
Publisher	Springer
Pages	101-111
Number of pages	11
ISBN (Electronic)	978-3-319-92016-0
ISBN (Print)	978-3-319-92015-3
DOIs	https://doi.org/10.1007/978-3-319-92016-0_10
Publication status	Published - May 2018
Event	14th International Conference on Artificial Intelligence Applications and Innovations - Rhodes, Greece Duration: 25 May 2018 → 27 May 2018 http://easyconferences.eu/aiai2018/

Publication series

Name	IFIP Advances in Information and Communication Technology
Publisher	Springer
Volume	520
ISSN (Print)	1868-4238
ISSN (Electronic)	1868-422X

Conference

Conference	14th International Conference on Artificial Intelligence Applications and Innovations
Abbreviated title	AIAI 2018
Country/Territory	Greece
City	Rhodes
Period	25/05/18 → 27/05/18
Internet address	http://easyconferences.eu/aiai2018/

Access to Document

10.1007/978-3-319-92016-0_10

Detecting Question Intention Using a K-Nearest Neighbor Based Approach
The final authenticated version is available online at: http://dx.doi.org/10.1007%2F978-3-319-92016-0_10.
Accepted author manuscript (Post-print), 269 KB

Cite this

Mohasseb, A., Bader-El-Den, M., & Cocea, M. (2018). Detecting question intention using a K-Nearest neighbor based approach. In L. Iliadis, I. Maglogiannis, & V. Plagianakos (Eds.), Artificial Intelligence Applications and Innovations: AIAI 2018 IFIP WG 12.5 International Workshops, SEDSEAL, 5G-PINE, MHDW, and HEALTHIOT, Rhodes, Greece, May 25-27, 2018, Proceedings (pp. 101-111). (IFIP Advances in Information and Communication Technology; Vol. 520). Springer. https://doi.org/10.1007/978-3-319-92016-0_10

Mohasseb, Alaa ; Bader-El-Den, Mohamed ; Cocea, Mihaela. / Detecting question intention using a K-Nearest neighbor based approach. Artificial Intelligence Applications and Innovations: AIAI 2018 IFIP WG 12.5 International Workshops, SEDSEAL, 5G-PINE, MHDW, and HEALTHIOT, Rhodes, Greece, May 25-27, 2018, Proceedings. editor / Lazaros Iliadis ; Ilias Maglogiannis ; Vassilis Plagianakos. Springer, 2018. pp. 101-111 (IFIP Advances in Information and Communication Technology).

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abstract = "The usage of question answering systems is increasing daily. People constantly use question answering systems in order to find the right answer for different kinds of information, but the abundance of available data has made the process of obtaining relevant information challenging in terms of processing and analyzing it. Many questions classification techniques have been proposed with the aim of helping in understanding the actual intent of the user{\textquoteright}s question. In this research, we have categorized different question types through introducing question type syntactical patterns for detecting question intention. In addition, a k-nearest neighbor based approach has been developed for question classification. Experiments show that our approach has a good level of accuracy in identifying different question types.",

author = "Alaa Mohasseb and Mohamed Bader-El-Den and Mihaela Cocea",

year = "2018",

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editor = "Lazaros Iliadis and Ilias Maglogiannis and Vassilis Plagianakos",

booktitle = "Artificial Intelligence Applications and Innovations",

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Mohasseb, A , Bader-El-Den, M & Cocea, M 2018, Detecting question intention using a K-Nearest neighbor based approach. in L Iliadis, I Maglogiannis & V Plagianakos (eds), Artificial Intelligence Applications and Innovations: AIAI 2018 IFIP WG 12.5 International Workshops, SEDSEAL, 5G-PINE, MHDW, and HEALTHIOT, Rhodes, Greece, May 25-27, 2018, Proceedings. IFIP Advances in Information and Communication Technology, vol. 520, Springer, pp. 101-111, 14th International Conference on Artificial Intelligence Applications and Innovations, Rhodes, Greece, 25/05/18. https://doi.org/10.1007/978-3-319-92016-0_10

Detecting question intention using a K-Nearest neighbor based approach. / Mohasseb, Alaa ; Bader-El-Den, Mohamed ; Cocea, Mihaela.

Artificial Intelligence Applications and Innovations: AIAI 2018 IFIP WG 12.5 International Workshops, SEDSEAL, 5G-PINE, MHDW, and HEALTHIOT, Rhodes, Greece, May 25-27, 2018, Proceedings. ed. / Lazaros Iliadis; Ilias Maglogiannis; Vassilis Plagianakos. Springer, 2018. p. 101-111 (IFIP Advances in Information and Communication Technology; Vol. 520).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Cocea, Mihaela

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N2 - The usage of question answering systems is increasing daily. People constantly use question answering systems in order to find the right answer for different kinds of information, but the abundance of available data has made the process of obtaining relevant information challenging in terms of processing and analyzing it. Many questions classification techniques have been proposed with the aim of helping in understanding the actual intent of the user’s question. In this research, we have categorized different question types through introducing question type syntactical patterns for detecting question intention. In addition, a k-nearest neighbor based approach has been developed for question classification. Experiments show that our approach has a good level of accuracy in identifying different question types.

AB - The usage of question answering systems is increasing daily. People constantly use question answering systems in order to find the right answer for different kinds of information, but the abundance of available data has made the process of obtaining relevant information challenging in terms of processing and analyzing it. Many questions classification techniques have been proposed with the aim of helping in understanding the actual intent of the user’s question. In this research, we have categorized different question types through introducing question type syntactical patterns for detecting question intention. In addition, a k-nearest neighbor based approach has been developed for question classification. Experiments show that our approach has a good level of accuracy in identifying different question types.

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Mohasseb A , Bader-El-Den M , Cocea M. Detecting question intention using a K-Nearest neighbor based approach. In Iliadis L, Maglogiannis I, Plagianakos V, editors, Artificial Intelligence Applications and Innovations: AIAI 2018 IFIP WG 12.5 International Workshops, SEDSEAL, 5G-PINE, MHDW, and HEALTHIOT, Rhodes, Greece, May 25-27, 2018, Proceedings. Springer. 2018. p. 101-111. (IFIP Advances in Information and Communication Technology). Epub 2018 May 22. doi: 10.1007/978-3-319-92016-0_10

Detecting question intention using a K-Nearest neighbor based approach

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