Paper Info
Title
A Robust Passage Retrieval Algorithm for Video Question Answering
Abstract
In this paper, we present a robust passage retrieval algorithm to extend the conventional text question answering (Q/A) to videos. Users interact with our videoQ/A system through natural language queries, while the top-ranked passage fragments with associated video clips are returned as answers. We compare our method with five of the high-performance ranking algorithms that are portable to different languages and domains. The experiments were evaluated with 75.3 h of Chinese videos and 253 questions. The experimental results showed that our method outperformed the second best retrieval model (language models) in relatively 1.43% in mean reciprocal rank (MRR) score and 11.36% when employing a Chinese word segmentation tool. By adopting the initial retrieval results from the retrieval models, our method yields an improvement of at least 5.94% improvement in MRR score. This makes it very attractive for the Asia-like languages since the use of a well-developed word tokenizer is unnecessary.
Year
DOI
Venue
2008
10.1109/TCSVT.2008.2002831
IEEE Trans. Circuits Syst. Video Techn.
Keywords
Field
DocType
mrr score,different language,video clips,text question answering,video question answering (videoq/a),mean reciprocal rank,initial retrieval result,language models,chinese videos,video question answering,best retrieval model,method yield,asia-like languages,chinese word segmentation tool,word tokenizer,chinese video,multimedia retrieval,robust passage retrieval algorithm,asia-like language,natural language processing,question answering (q/a),video retrieval,retrieval model,query processing,natural languages,data mining,robustness,image retrieval,search engines,indexing terms,language model,natural language,information retrieval,question answering
Learning to rank,Question answering,Computer science,Image retrieval,Speech recognition,Text segmentation,Mean reciprocal rank,Natural language,Natural language processing,Artificial intelligence,Lexical analysis,Language model
Journal
Volume
Issue
ISSN
18
10
1051-8215
Citations 
PageRank 
References 
11
0.58
40
Authors
2
Name
Order
Citations
PageRank
Yu-Chieh Wu124723.16
Jie-Chi Yang235043.91