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NAACL
2007

Data-Driven Graph Construction for Semi-Supervised Graph-Based Learning in NLP

11 years 3 months ago
Data-Driven Graph Construction for Semi-Supervised Graph-Based Learning in NLP
Graph-based semi-supervised learning has recently emerged as a promising approach to data-sparse learning problems in natural language processing. All graph-based algorithms rely on a graph that jointly represents labeled and unlabeled data points. The problem of how to best construct this graph remains largely unsolved. In this paper we introduce a data-driven method that optimizes the representation of the initial feature space for graph construction by means of a supervised classifier. We apply this technique in the framework of label propagation and evaluate it on two different classification tasks, a multi-class lexicon acquisition task and a word sense disambiguation task. Significant improvements are demonstrated over both label propagation using conventional graph construction and state-of-the-art supervised classifiers.
Andrei Alexandrescu, Katrin Kirchhoff
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2007
Where NAACL
Authors Andrei Alexandrescu, Katrin Kirchhoff
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