A normalized-cut alignment model for mapping hierarchical semantic structures onto spoken documents

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Proceedings titleProceedings of the Fifteenth Conference on Computational Natural Language Learning (CoNLL '11)
ConferenceFifteenth Conference on Computational Natural Language Learning, June 23-24, 2011, Portland, Oregon, USA
Pages210218; # of pages: 9
AbstractWe propose a normalized-cut model for the problem of aligning a known hierarchical browsing structure, e.g., electronic slides of lecture recordings, with the sequential transcripts of the corresponding spoken documents, with the aim to help index and access the latter. This model optimizes a normalizedcut graph-partitioning criterion and considers local tree constraints at the same time. The experimental results show the advantage of this model over Viterbi-like, sequential alignment, under typical speech recognition errors.
Publication date
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NPARC number18548399
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Record identifier5a994272-cabf-4844-b8d8-2ae51226a37e
Record created2011-09-08
Record modified2016-05-09
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