Paper Info
Title
Toward Multimedia: A String Pattern-Based Passage Ranking Model for Video Question Answering
Abstract
In this paper, we present a new string pat- tern matching-based passage ranking al- gorithm for extending traditional text- based QA toward videoQA. Users interact with our videoQA system through natural language questions, while our system re- turns passage fragments with correspond- ing video clips as answers. We collect 75.6 hours videos and 253 Chinese ques- tions for evaluation. The experimental re- sults showed that our method outperformed six top-performed ranking models. It is 10.16% better than the sec- ond best method (language model) in rela- tively MRR score and 6.12% in precision rate. Besides, we also show that the use of a trained Chinese word segmentation tool did decrease the overall videoQA per- formance where most ranking algorithms dropped at least 10% in relatively MRR, precision, and answer pattern recall rates.
Year
Venue
Keywords
2007
HLT-NAACL
natural language,language model,question answering
Field
DocType
Citations 
Learning to rank,Ranking SVM,Computer science,Artificial intelligence,Natural language processing,Language model,Question answering,Information retrieval,Ranking,Text segmentation,Natural language,Pattern matching,Machine learning
Conference
4
PageRank 
References 
Authors
0.41
22
2
Name
Order
Citations
PageRank
Yu-Chieh Wu124723.16
Jie-Chi Yang235043.91