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This paper presents the introduction of WordNet semantic classes in a dependency parser, obtaining improvements on the full Penn Treebank for the first time. We tried different combinations of some basic semantic classes and word sense disambiguation algorithms. Our experiments show that selecting the adequate combination of semantic features on development data is key for success. | Improving Dependency Parsing with Semantic Classes Eneko Agirre Kepa Bengoetxea Koldo Gojenola Joakim Nivre Department of Computer Languages and Systems University of the Basque Country UPV EHU Department of Linguistics and Philosophy Uppsala University e.agirre kepa.bengoetxea koldo.gojenola @ehu.es joakim.nivre@lingfil.uu.se Abstract This paper presents the introduction of WordNet semantic classes in a dependency parser obtaining improvements on the full Penn Treebank for the first time. We tried different combinations of some basic semantic classes and word sense disambiguation algorithms. Our experiments show that selecting the adequate combination of semantic features on development data is key for success. Given the basic nature of the semantic classes and word sense disambiguation algorithms used we think there is ample room for future improvements. 1 Introduction Using semantic information to improve parsing performance has been an interesting research avenue since the early days of NLP and several research works have tried to test the intuition that semantics should help parsing as can be exemplified by the classical PP attachment experiments Ratnaparkhi 1994 . Although there have been some significant results see Section 2 this issue continues to be elusive. In principle dependency parsing offers good prospects for experimenting with word-to-word-semantic relationships. We present a set of experiments using semantic classes in dependency parsing of the Penn Treebank PTB . We extend the tests made in Agirre et al. 2008 who used different types of semantic information obtaining significant improvements in two constituency parsers showing how semantic information helps in constituency parsing. As our baseline parser we use MaltParser Nivre 2006 . We will evaluate the parser on both the full PTB Marcus et al. 1993 and on a sense- 699 annotated subset of the Brown Corpus portion of PTB in order to investigate the upper bound performance of the models given .
