Paper: Joint Training of Dependency Parsing Filters through Latent Support Vector Machines

ACL ID P11-2035
Title Joint Training of Dependency Parsing Filters through Latent Support Vector Machines
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

Graph-based dependency parsing can be sped up significantly if implausible arcs are elim- inated from the search-space before parsing begins. State-of-the-art methods for arc fil- tering use separate classifiers to make point- wisedecisionsaboutthetree; theylabeltokens with roles such as root, leaf, or attaches-to- the-left, and then filter arcs accordingly. Be- cause these classifiers overlap substantially in their filtering consequences, we propose to train them jointly, so that each classifier can focus on the gaps of the others. We inte- grate the various pointwise decisions as latent variables in a single arc-level SVM classifier. This novel framework allows us to combine nine pointwise filters, and adjust their sensi- tivity using a shared threshold based on arc length. Our system fi...