Arabic Question Answering with Dialogue Support

Waheeb Ahmed; Babu Anto P

Arabic Question Answering with Dialogue Support

Waheeb Ahmed * §

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Babu Anto P

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§ Kannur University

Arabic Question Answering with Dialogue Support

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Abstract

Question Answering (QA) system is a combination of Information Retrieval(IR) and Natural Language Processing (NLP) techniques. It returns a specific answer in response to user question. However, a system that can interact with the user to clarify and refine the answer is required. We propose QA system that adopts a user model for adaptation and a dialogue interface for interaction with the user combined with information retrieval and natural language techniques for Arabic Language. Our system will be able to handle users’ questions in natural language and to present answers in in respect to the user’s preferences and expected needs. The system achieved a precision of 82.05% and a dialogue success rate of 71.6%. The result is highly promising. As an extension for the present work, we need to make the system more adaptive and capable to learn and evolve with every new interactive scenario.

References

12 Cites in Article

Reference Format

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W Ahmed,B Anto (2016). Question Analysis for Arabic Question Answering Systems.
W Ahmed,B Anto (2016). Classification of Arabic Questions Using Multinomial Naive Bayes and Support Vector Machines.
Hani Al-Chalabi,Santosh Ray,Khaled Shaalan (2015). Semantic Based Query Expansion for Arabic Question Answering Systems.
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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Waheeb Ahmed, Babu Anto P. 2017. "Arabic Question Answering with Dialogue Support". Global Journal of Computer Science and Technology - H: Information & Technology GJCST-H Volume 17 (GJCST Volume 17 Issue H1).

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GJCST Volume 17 Issue H1

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Not Found

Classification

GJCST-H Classification J.4

K.4.2

Submission ReceivedDecember 9, 2016
Peer Review Double Blind
Handling Editor
Accepted January 5, 2017
Published January 15, 2017

Version of record

v1.2

Issue date

March 13, 2017

Language

English

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Article Matrices

Total Score: 142

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Waheeb Ahmed, Babu Anto P (PhD/Dr. count: 0)

View Count (all-time): 431

Total Views (Real + Logic): 2970

Total Downloads (simulated): 156

Publish Date: 2017 01, Sun

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Total Views: 3K

Total Downloads: 156

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