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.
REF propose a graphbased semi-supervised label propagation algorithm for acquiring open-domain labeled classes and their instances from a combination of unstructured and structured text sources.
Weakly-Supervised Acquisition of Labeled Class Instances using Graph Random Walks
Conference On Empirical Methods In Natural Language Processing
Computer Science