A Neural Network Parser that Handles Sparse Data.
James Henderson · 2000
Previous work has demonstrated the viability of a particular neural network architecture, Simple Synchrony Networks, for syntactic parsing [6]. Here we present additional results on the performance of this type of parser, including direct comparisons on the same dataset with a standard statistical parsing method, Probabilistic Context Free Grammars. We focus these experiments on demonstrating one of the main advantages of the SSN parser over the PCFG, handling sparse data. We use smaller datasets than are typically used with statistical methods, resulting in the PCFG nding parses for under half of the test sentences, while the SSN nds parses for all sentences. Even on the PCFG's parsed half, the SSN performs better than the PCFG, as measure by recall and precision on both constituents and a dependency-like measure. 1