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. 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 senseannotated subset of the Brown Corpus portion of PTB, in order to investigate the upper bound performance of the models given gold-standard sense information, as in Agirre et al. (2008) . Agirre et al. (2008) trained two state-of-the-art statistical parsers (Charniak, 2000; Bikel, 2004 ) on semantically-enriched input, where content words had been substituted with their semantic classes. This was done trying to overcome the limitations of lexicalized approaches to parsing (Magerman, 1995; Collins, 1996; Charniak, 1997; Collins, 2003) , where related words, like scissors and knife cannot be generalized. This simple method allowed incorporating lexical semantic information into the parser. They tested the parsers in both a full parsing and a PP attachment context. The experiments showed that semantic classes gave significant improvement relative to the baseline, demonstrating that a simplistic approach to incorporating lexical semantics into a parser significantly improves its performance. This work presented the first results over both WordNet and the Penn Treebank to show that semantic processing helps parsing. Collins (2000) tested a combined parsing/word sense disambiguation model based in WordNet which did not obtain improvements in parsing. presented a semisupervised method for training dependency parsers, using word clusters derived from a large unannotated corpus as features. They demonstrate the effectiveness of the approach in a series of dependency parsing experiments on PTB and the Prague Dependency Treebank, showing that the cluster-based features yield substantial gains in performance across a wide range of conditions. Suzuki et al. (2009) also experiment with the same method combined with semi-supervised learning. 699
Similarly, for dependency parsing, REF utilized the English WordNet semantic classes and improved parsing accuracies.
Improving Dependency Parsing with Semantic Classes
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
Computer Science