期刊名称：International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
印刷版ISSN：2278-1323
出版年度：2014
卷号：3
期号：3
页码：984-989
出版社：Shri Pannalal Research Institute of Technolgy
摘要：Question Answering is a technique for automatically answering a question in natural language.This question answering services have attained great success over the past years.Despite their great success,existing cQA forums mostly support only the textual answers.These textual answers are not provide the sufficient information.In this paper,we propose a scheme that is able to enhance the textual answers in QA with appropriate media data.For retrieving the media information such as image and video we introduce a semantic web Technology.This approach automatically determines which type of media data should be added for the textual answer.It then automatically collects data from the web to enrich the answer. By processing a large set of QA pairs and adding them to a pool, our approach can enable a novel multimedia question answering (MMQA) approach as users can find multimedia answers by matching their questions with those in the pool. Different from a lot of MMQA research efforts that attempt to directly answer questions with image and video data, our approach is built based on community-contributed textual answers and thus it is able to deal with more complex questions. We have conducted extensive experiments on a multisource QA dataset. The results demonstrate the effectiveness of our approach.
关键词：semantic web ;Question answering; cQA; ; medium selection; reranking
