Parsing Noun Phrase Structure with CCG

Proceedings of ACL-08: HLT, pages 335–343, Columbus, Ohio, USA, June 2008. c⃝2008 Association for Computational Linguistics Parsing Noun Phrase Structure with CCG David Vadas and James R. Curran School of Information Technologies University of Sydney NSW 2006, Australia {dvadas1, james}@it.usyd.edu.au Abstract Statistical parsing of noun phrase (NP) struc- ture has been hampered by a lack of gold- standard data. This is a significant problem for CCGbank, where binary branching NP deriva- tions are often incorrect, a result of the auto- matic conversion from the Penn Treebank. We correct these errors in CCGbank using a gold-standard corpus of NP structure, result- ing in a much more accurate corpus. We also implement novel NER features that generalise the lexical information needed to parse NPs and provide important semantic information. Finally, evaluating against DepBank demon- strates the effectiveness of our modified cor- pus and novel features, with an increase in parser performance of 1.51%. 1 Introduction Internal noun phrase (NP) structure is not recovered by a number of widely-used parsers, e.g. Collins (2003). This is because their training data, the Penn Treebank (Marcus et al., 1993), does not fully anno- tate NP structure. The flat structure described by the Penn Treebank can be seen in this example: (NP (NN lung) (NN cancer) (NNS deaths)) CCGbank (Hockenmaier and Steedman, 2007) is the primary English corpus for Combinatory Cate- gorial Grammar (CCG) (Steedman, 2000) and was created by a semi-automatic conversion from the Penn Treebank. However, CCG is a binary branch- ing grammar, and as such, cannot leave NP structure underspecified. Instead, all NPs were made right- branching, as shown in this example: (N (N/N lung) (N (N/N cancer) (N deaths) ) ) This structure is correct for most English NPs and is the best solution that doesn’t require manual re- annotation. However, the resulting derivations often contain errors. This can be seen in the previous ex- ample, where lung cancer should form a con- stituent, but does not. The first contribution of this paper is to correct these CCGbank errors. We apply an automatic con- version process using the gold-standard NP data an- notated by Vadas and Curran (2007a). Over a quar- ter of the sentences in CCGbank need to be altered, demonstrating the magnitude of the NP problem and how important it is that these errors are fixed. We then run a number of parsing experiments us- ing our new version of the CCGbank corpus. In particular, we implement new features using NER tags from the BBN Entity Type Corpus (Weischedel and Brunstein, 2005). These features are targeted at improving the recovery of NP structure, increasing parser performance by 0.64% F-score. Finally, we evaluate against DepBank (King et al., 2003). This corpus annotates internal NP structure, and so is particularly relevant for the changes we have made to CCGbank. The CCG parser now recov- ers additional structure learnt from our NP corrected corpus, increasing performance by 0.92%. Applying the NER features results in a total increase of 1.51%. This work allows parsers trained …

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