Optimal Shift-Reduce Constituent Parsing with Structured Perceptron

Le Quang Thang, Hiroshi Noji, Yusuke Miyao


Abstract

We present a constituent shift-reduce parser with a structured perceptron that finds the optimal parse in a practical runtime. The key ideas are new feature templates that facilitate state merging of dynamic programming and A* search. Our system achieves 91.1 F1 on a standard English experiment, a level which cannot be reached by other beam-based systems even with large beam sizes.