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Semi-Supervised Semantic Role Labeling via Structural Alignment
2012
Computational Linguistics
Large-scale annotated corpora are a prerequisite to developing high-performance semantic role labeling systems. Unfortunately, such corpora are expensive to produce, limited in size, and may not be representative. Our work aims to reduce the annotation effort involved in creating resources for semantic role labeling via semi-supervised learning. The key idea of our approach is to find novel instances for classifier training based on their similarity to manually labeled seed instances. The
doi:10.1162/coli_a_00087
fatcat:2cu3d2t4r5addh6zegg4qpse6a