Paper: Improving Dependency Parsers with Supertags

ACL ID E14-4030
Title Improving Dependency Parsers with Supertags
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2014

Transition-based dependency parsing sys- tems can utilize rich feature representa- tions. However, in practice, features are generally limited to combinations of lexi- cal tokens and part-of-speech tags. In this paper, we investigate richer features based on supertags, which represent lexical tem- plates extracted from dependency struc- ture annotated corpus. First, we develop two types of supertags that encode infor- mation about head position and depen- dency relations in different levels of granu- larity. Then, we propose a transition-based dependency parser that incorporates the predictions from a CRF-based supertagger as new features. On standard English Penn Treebank corpus, we show that our su- pertag features achieve parsing improve- ments of 1.3% in unlabeled attachment, 2.07% roo...