Improving Dependency Parsers using Combinatory Categorial Grammar

Subcategorization information is a useful feature in dependency parsing. In this paper, we explore a method of incorpo-rating this information via Combinatory Categorial Grammar (CCG) categories from a supertagger. We experiment with two popular dependency parsers (Malt and MST) for two languages: English and Hindi. For both languages, CCG categories improve the overall accuracy of both parsers by around 0.3-0.5 % in all experiments. For both parsers, we see larger improvements specifically on dependencies at which they are known to be weak: long distance dependencies for Malt, and verbal arguments for MST. The result is particularly interesting in the case of the fast greedy parser (Malt), since im-proving its accuracy without significantly compromising speed is relevant for large scale applications such as parsing the web.