Constituent Parsing by Classification
par/by Joseph Turian
Computer Science Department
New York University
We present an approach to constituent parsing, which is driven
by classifiers induced to minimize a single regularized objective.
It is the first discriminatively-trained constituent parser to
surpass the Collins (2003) parser without using a generative model.
Our primary contribution is simplifying the human effort required for
feature engineering. Our model can incorporate arbitrary features of the
input and parse state. Feature selection and feature construction occur
automatically, as part of learning. We define a set of fine-grained
atomic features, and let the learner induce informative compound
features. Our learning approach includes several novel approximations and
optimizations which improve the efficiency of discriminative training. We
introduce greedy completion, a new agenda-driven search strategy designed
to find low-cost solutions given a limit on search effort. Experiments
demonstrate the flexibility of our approach, which has also been applied
to machine translation (Wellington et al. AMTA 2006, Turian et al. NIPS
2007).