Dependency Language Models for Transition-based Dependency Parsing
This work addresses parsing accuracy for natural language processing tasks, but it is incremental as it builds upon an existing strong parser with minor feature additions.
The authors tackled the problem of improving transition-based dependency parsing accuracy by integrating dependency language models from a large parsed corpus, resulting in state-of-the-art accuracy on Chinese data and competitive results on English data with absolute improvements of 1.0 and 0.5 points in UAS, respectively.
In this paper, we present an approach to improve the accuracy of a strong transition-based dependency parser by exploiting dependency language models that are extracted from a large parsed corpus. We integrated a small number of features based on the dependency language models into the parser. To demonstrate the effectiveness of the proposed approach, we evaluate our parser on standard English and Chinese data where the base parser could achieve competitive accuracy scores. Our enhanced parser achieved state-of-the-art accuracy on Chinese data and competitive results on English data. We gained a large absolute improvement of one point (UAS) on Chinese and 0.5 points for English.