Speech for multimedia information retrieval
Proceedings of the 8th annual ACM symposium on User interface and software technology
The handbook of multimedia information management
Introduction to Algorithms
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Multiple media correlation: theory and applications
Multiple media correlation: theory and applications
A portable algorithm for mapping bitext correspondence
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Char_align: a program for aligning parallel texts at the character level
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Similarity-based estimation of word cooccurrence probabilities
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
K-vec: a new approach for aligning parallel texts
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
The IMAGETCL multimedia algorithm development system
TCLTK'97 Proceedings of the 5th conference on Annual Tcl/Tk Workshop 1997 - Volume 5
Word alignment of English-Chinese bilingual corpus based on chunks
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
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Parallel Text Alignment (PTA) is the problem of automatically aligning content in multiple text documents originating or derived from the same source. The implications of this result in improving multimedia data access in digital library applications range from facilitating the analysis of multiple English language translations of classical texts to enabling the on-demand and random comparison of multiple transcriptions derived from a given audio stream, or associated with a given stream of video, audio, or images. In this paper we give an efficient algorithm for achieving such an alignment, and demonstrate its use with two applications. This result is an application of the new framework of Cross-Modal Information Retrieval recently developed at Dartmouth.