Split-Based Algorithm for Weighted Context-Free Grammar Induction

Abstract

The split-based method in a weighted context-free grammar (WCFG) induction was formalised and verified on a comprehensive set of context-free languages. WCFG is learned using a novel grammatical inference method. The proposed method learns WCFG from both positive and negative samples, whereas the weights of rules are estimated using a novel Inside–Outside Contrastive Estimation algorithm. The results showed that our approach outperforms in terms of F1 scores of other state-of-the-art methods.

Keywords

• grammar inference
• weighted context-free grammar
• split algorithm
• unsupervised learning