Adding Noun Phrase Structure to the Penn Treebank

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 240–247, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics Adding Noun Phrase Structure to the Penn Treebank David Vadas and James R. Curran School of Information Technologies University of Sydney NSW 2006, Australia � dvadas1, james � @it.usyd.edu.au Abstract The Penn Treebank does not annotate within base noun phrases (NPs), commit- ting only to flat structures that ignore the complexity of English NPs. This means that tools trained on Treebank data cannot learn the correct internal structure of NPs. This paper details the process of adding gold-standard bracketing within noun phrase in the Penn Treebank.each We then examine the consistency and reliabil- ity of our annotations. Finally, we use this resource to determine NP structure using several statistical approaches, thus demonstrating the utility of the corpus. This adds detail to the Penn Treebank that is necessary for many NLP applications. 1 Introduction The Penn Treebank (Marcus et al., 1993) is perhaps the most influential resource in Natural Language Processing (NLP). It is used as a standard train- ing and evaluation corpus in many syntactic analysis tasks, ranging from part of speech (POS) tagging and chunking, to full parsing. Unfortunately, the Penn Treebank does not anno- tate the internal structure of base noun phrases, in- stead leaving them flat. This significantly simplified and sped up the manual annotation process. Therefore, any system trained on Penn Treebank data will be unable to model the syntactic and se- mantic structure inside base-NPs. The following NP is an example of the flat struc- ture of base-NPs within the Penn Treebank: (NP (NNP Air) (NNP Force) (NN contract)) Air Force is a specific entity and should form a sep- arate constituent underneath the NP, as in our new annotation scheme: (NP (NML (NNP Air) (NNP Force)) (NN contract)) We use NML to specify that Air Force together is a nominal modifier of contract. Adding this annota- tion better represents the true syntactic and seman- tic structure, which will improve the performance of downstream NLP systems. Our main contribution is a gold-standard labelled bracketing for every ambiguous noun phrase in the Penn Treebank. We describe the annotation guide- lines and process, including the use of named en- tity data to improve annotation quality. We check the correctness of the corpus by measuring inter- annotator agreement, by reannotating the first sec- tion, and by comparing against the sub-NP structure in DepBank (King et al., 2003). We also give an analysis of our extended Tree- bank, quantifying how much structure we have added, and how it is distributed across NPs. Fi- nally, we test the utility of the extended Treebank for training statistical models on two tasks: NP bracket- ing (Lauer, 1995; Nakov and Hearst, 2005) and full parsing (Collins, 1999). This new resource will allow any system or anno- tated corpus developed from the Penn Treebank, to represent noun phrase structure more accurately. 240 2 Motivation Many approaches to identifying base noun …

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