High Quality Dependency Selection from Automatic Parses

Many NLP tasks such as question answering and knowledge acquisition are tightly dependent on dependency parsing. Dependency parsing accuracy is always decisive for the performance of subsequent tasks. Therefore, reducing dependency parsing errors or selecting high quality dependencies is a primary issue. In this paper, we present a supervised approach for automatically selecting high quality dependencies from automatic parses. Experimental results on three different languages show that our approach can effectively select high quality dependencies from the result analyzed by a dependency parser.