A language modeling approach to information retrieval
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Video OCR: indexing digital new libraries by recognition of superimposed captions
Multimedia Systems - Special section on video libraries
Quantitative evaluation of passage retrieval algorithms for question answering
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
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ISMSE '04 Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering
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HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 1 - Volume 01
Question answering passage retrieval using dependency relations
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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ACM Transactions on Asian Language Information Processing (TALIP)
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A new passage ranking algorithm for video question answering
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A natural language approach to content-based video indexing and retrieval for interactive e-learning
IEEE Transactions on Multimedia
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IEEE Transactions on Circuits and Systems for Video Technology
A comprehensive method for multilingual video text detection, localization, and extraction
IEEE Transactions on Circuits and Systems for Video Technology
From text question-answering to multimedia QA on web-scale media resources
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
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Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
A support vector machine-based context-ranking model for question answering
Information Sciences: an International Journal
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This article presents a bilingual video question answering (QA) system, namely BVideoQA, which allows users to retrieve Chinese videos through English or Chinese natural language questions. Our method first extracts an optimal one-to-one string pattern matching according to the proposed dense and long N-gram match. On the basis of the matched string patterns, it gives a passage score based on our term-weighting scheme. The main contributions of this approach to multimedia information retrieval literatures include: (a) development of a truly bilingual video QA system, (b) presentation of a robust bilingual passage retrieval algorithm to handle no-word-boundary languages such as Chinese and Japanese, (c) development of a large-scale bilingual video QA corpus for system evaluation, and (d) comparisons of seven top-performing retrieval methods under the fair conditions. The experimental studies indicate that our method is superior to other existing approaches in terms of precision and main rank reciprocal rates. When ported to English, encouraging empirical results also are obtained. Our method is very important to Asian-like languages since the development of a word tokenizer is optional. © 2009 Wiley Periodicals, Inc.