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Authors: Wenjing Yang and Jie Wang

Affiliation: University of Massachusetts, United States

Keyword(s): Question-Answering, Content Extraction, LDA Clustering, Keyword Extraction.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Clustering and Classification Methods ; Context Discovery ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Soft Computing ; Symbolic Systems ; Web Mining

Abstract: Community-based question-answering web sites (CQAW) contain rich collections of question-answer pages, where a single question often has multiple answers written by different authors with different aspects. We study how to harvest new question-answer pairs from CQAWs so that each question-answer pair addresses just one aspect that are suitable for chatbots over a specific domain. In particular, we first extract all answers to a question from a CQAW site using DOM-tree similarities and features of answer areas, and then cluster the answers using LDA. Next, we form a sub-question for each cluster using a small number of top keywords in the given cluster with the keywords in the original question. We select the best answer to the sub-question based on user ratings and similarities of answers to the sub-question. Experimental results show that our approach is effective.

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Paper citation in several formats:
Yang, W. and Wang, J. (2017). Generating Appropriate Question-Answer Pairs for Chatbots using Data Harvested from Community-based QA Sites. In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2017) - KDIR; ISBN 978-989-758-271-4; ISSN 2184-3228, SciTePress, pages 342-349. DOI: 10.5220/0006578603420349

@conference{kdir17,
author={Wenjing Yang and Jie Wang},
title={Generating Appropriate Question-Answer Pairs for Chatbots using Data Harvested from Community-based QA Sites},
booktitle={Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2017) - KDIR},
year={2017},
pages={342-349},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006578603420349},
isbn={978-989-758-271-4},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2017) - KDIR
TI - Generating Appropriate Question-Answer Pairs for Chatbots using Data Harvested from Community-based QA Sites
SN - 978-989-758-271-4
IS - 2184-3228
AU - Yang, W.
AU - Wang, J.
PY - 2017
SP - 342
EP - 349
DO - 10.5220/0006578603420349
PB - SciTePress

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