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Improving Dependency Parsers with Supertags
2014
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers
Transition-based dependency parsing systems can utilize rich feature representations. However, in practice, features are generally limited to combinations of lexical tokens and part-of-speech tags. In this paper, we investigate richer features based on supertags, which represent lexical templates extracted from dependency structure annotated corpus. First, we develop two types of supertags that encode information about head position and dependency relations in different levels of granularity.
doi:10.3115/v1/e14-4030
dblp:conf/eacl/OuchiDM14
fatcat:fldbauaeibfyzdekc3srzpknwy