Word Sense Disambiguation for All Words using Tree-Structured Conditional Random Fields

We propose a supervised word sense disambiguation (WSD) method using tree-structured conditional random fields (TCRFs). By applying TCRFs to a sentence described as a dependency tree structure, we conduct WSD as a labeling problem on tree structures. To incorporate dependencies between word senses, we introduce a set of features on tree edges, in combination with coarse-grained tagsets, and show that these contribute to an improvement in WSD accuracy. We also show that the tree-structured model outperforms the linear-chain model. Experiments on the SENSEVAL-3 data set show that our TCRF model performs comparably with state-of-the-art WSD systems.