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Graph-Based Semi-Supervised Conditional Random Fields For Spoken Language Understanding Using Unaligned Data
lib:7442babdd4e42ddb (v1.0.0)
Authors: Mohammad Aliannejadi,Masoud Kiaeeha,Shahram Khadivi,Saeed Shiry Ghidary
Where published: ALTA 2014 11
ArXiv: 1701.08533
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1701.08533v1
We experiment graph-based Semi-Supervised Learning (SSL) of Conditional Random Fields (CRF) for the application of Spoken Language Understanding (SLU) on unaligned data. The aligned labels for examples are obtained using IBM Model. We adapt a baseline semi-supervised CRF by defining new feature set and altering the label propagation algorithm. Our results demonstrate that our proposed approach significantly improves the performance of the supervised model by utilizing the knowledge gained from the graph.
Where published: ALTA 2014 11
ArXiv: 1701.08533
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1701.08533v1
We experiment graph-based Semi-Supervised Learning (SSL) of Conditional Random Fields (CRF) for the application of Spoken Language Understanding (SLU) on unaligned data. The aligned labels for examples are obtained using IBM Model. We adapt a baseline semi-supervised CRF by defining new feature set and altering the label propagation algorithm. Our results demonstrate that our proposed approach significantly improves the performance of the supervised model by utilizing the knowledge gained from the graph.
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