A graph-based semi-supervised learning for question semantic labeling

Authors:
Asli Celikyilmaz;Dilek Hakkani-Tur
Affiliations:
University of California, Berkeley;International Computer Science Institute, Berkeley, CA
Venue:
SS '10 Proceedings of the NAACL HLT 2010 Workshop on Semantic Search
Year:
2010

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Abstract

We investigate a graph-based semi-supervised learning approach for labeling semantic components of questions such as topic, focus, event, etc., for question understanding task. We focus on graph construction to handle learning with dense/sparse graphs and present Relaxed Linear Neighborhoods method, in which each node is linearly constructed from varying sizes of its neighbors based on the density/sparsity of its surrounding. With the new graph representation, we show performance improvements on syntactic and real datasets, primarily due to the use of unlabeled data and relaxed graph construction.