A reranking method for syntactic parsing with heterogeneous treebanks

In the field of natural language processing (NLP), there often exist multiple corpora with different annotation standards for the same task. In this paper, we take syntactic parsing as a case study and propose a reranking method which is able to make direct use of disparate treebanks simultaneously without using techniques such as treebank conversion. The method proceeds in three steps: 1) build parsers on individual treebanks; 2) use parsers independently to generate n-best lists for each sentence in test set; 3) rerank individual n-best lists which correspond to the same sentence by using consensus information exchanged among these n-best lists. Experimental results on two open Chinese treebanks show that our method significantly outperforms the baseline system by 0.84% and 0.53% respectively.