A Graph-Based Semi-Supervised Learning for Question Semantic Labeling

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.