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Can I read Semi-Supervised Semantic Role Labeling via Structural Alignment on EtoBox?
Semi-Supervised Semantic Role Labeling via Structural Alignment by Hagen Fürstenau; Mirella Lapata is a Computer Science article available to read on EtoBox.
What is Semi-Supervised Semantic Role Labeling via Structural Alignment about?
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 underlying assumption is that sentences that are similar in their lexical material and syntactic structure are likely to share a frame semantic analysis. We formalize the detection of similar sentences and the projection of role annotations as a graph alignment problem, which we solve exactly using integer linear programming. Experimental results on semantic role labeling show that the automatic annotations produced by our method improve performance over using hand-labeled instances alone.
Who reads Semi-Supervised Semantic Role Labeling via Structural Alignment?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Hagen Fürstenau; Mirella Lapata
- Publisher
- MIT Press - Journals
- Published
- 2012
- Language
- EN
- Field
- Computer Science (Physical Sciences)