Weakly-Supervised Acquisition of Labeled Class Instances using Graph Random Walks

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We present a graph-based semi-supervised label propagation algorithm for acquiring opendomain labeled classes and their instances from a combination of unstructured and structured text sources. This acquisition method significantly improves coverage compared to a previous set of labeled classes and instances derived from free text, while achieving comparable precision.

Partha Pratim Talukdar, Joseph Reisinger, Marius P

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EMNLP 2008 | Graph-based Semi-supervised Label | Natural Language Processing | Opendomain Labeled Classes | Structured Text Sources |

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Added	29 Oct 2010
Updated	29 Oct 2010
Type	Conference
Year	2008
Where	EMNLP
Authors	Partha Pratim Talukdar, Joseph Reisinger, Marius Pasca, Deepak Ravichandran, Rahul Bhagat, Fernando Pereira

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Weakly-Supervised Acquisition of Labeled Class Instances using Graph Random Walks

EMNLP 2008 | Graph-based Semi-supervised Label | Natural Language Processing | Opendomain Labeled Classes | Structured Text Sources |

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