Paper: A Normalized-Cut Alignment Model for Mapping Hierarchical Semantic Structures onto Spoken Documents

ACL ID W11-0324
Title A Normalized-Cut Alignment Model for Mapping Hierarchical Semantic Structures onto Spoken Documents
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2011
Authors

We 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 tran- scripts of the corresponding spoken docu- ments, with the aim to help index and access the latter. This model optimizes a normalized- cut graph-partitioning criterion and considers local tree constraints at the same time. The ex- perimental results show the advantage of this model over Viterbi-like, sequential alignment, under typical speech recognition errors.