2D Trie for Fast Parsing

In practical applications, decoding speed is very important. Modern structured learning technique adopts template based method to extract millions of features. Complicated templates bring about abundant features which lead to higher accuracy but more feature extraction time. We propose Two Dimensional Trie (2D Trie), a novel efficient feature indexing structure which takes advantage of relationship between templates: feature strings generated by a template are prefixes of the features from its extended templates. We apply our technique to Maximum Spanning Tree dependency parsing. Experimental results on Chinese Tree Bank corpus show that our 2D Trie is about 5 times faster than traditional Trie structure, making parsing speed 4.3 times faster.1