Abu Shawar, B and Atwell, ES (2009) Arabic question-answering via instance based learning from an FAQ corpus. In: Proceedings of the CL2009 International Conference on Corpus Linguistics. CL2009 International Conference on Corpus Linguistics, 20-23 Jul 2009, University of Liverpool, UK. UCREL, Lancaster University
Abstract
In this paper, we describe a way to access Arabic information using chatbot, without the need for sophisticated natural language processing or logical inference. FAQs are Frequently-Asked Questions documents, designed to capture the logical ontology of a given domain. Any Natural Language interface to an FAQ is constrained to reply with the given Answers, so there is no need for NL generation to recreate well-formed answers, or for deep analysis or logical inference to map user input questions onto this logical ontology; simple (but large) set of pattern-template matching rules will suffice. In previous research, this works properly with English and other European languages. In this paper, we try to see how the same chatbot will react in terms of Arabic FAQs. Initial results shows that 93% of answers were correct, but because of a lot of characteristics related to Arabic language, changing Arabic questions into other forms may lead to no answers.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2009, UCREL. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Chatbot; FAQs; information retrieval; question answering system |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 16 Jan 2015 12:26 |
Last Modified: | 19 Dec 2022 13:30 |
Published Version: | http://ucrel.lancs.ac.uk/publications/cl2009/ |
Status: | Published |
Publisher: | UCREL, Lancaster University |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:82302 |