Rebanking CCGbank for Improved NP Interpretation

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 207–215, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Rebanking CCGbank for improved NP interpretation Matthew Honnibal and James R. Curran School of Information Technologies University of Sydney NSW 2006, Australia {mhonn,james}@it.usyd.edu.au Johan Bos University of Groningen The Netherlands bos@meaningfactory.com Abstract Once released, treebanks tend to remain unchanged despite any shortcomings in their depth of linguistic analysis or cover- age of specific phenomena. Instead, sepa- rate resources are created to address such problems. In this paper we show how to improve the quality of a treebank, by in- tegrating resources and implementing im- proved analyses for specific constructions. We demonstrate this rebanking process by creating an updated version of CCG- bank that includes the predicate-argument structure of both verbs and nouns, base- NP brackets, verb-particle constructions, and restrictive and non-restrictive nominal modifiers; and evaluate the impact of these changes on a statistical parser. 1 Introduction Progress in natural language processing relies on direct comparison on shared data, discouraging improvements to the evaluation data. This means that we often spend years competing to reproduce partially incorrect annotations. It also encourages us to approach related problems as discrete tasks, when a new data set that adds deeper information establishes a new incompatible evaluation. Direct comparison has been central to progress in statistical parsing, but it has also caused prob- lems. Treebanking is a difficult engineering task: coverage, cost, consistency and granularity are all competing concerns that must be balanced against each other when the annotation scheme is devel- oped. The difficulty of the task means that we ought to view treebanking as an ongoing process akin to grammar development, such as the many years of work on the ERG (Flickinger, 2000). This paper demonstrates how a treebank can be rebanked to incorporate novel analyses and infor- mation from existing resources. We chose to work on CCGbank (Hockenmaier and Steedman, 2007), a Combinatory Categorial Grammar (Steedman, 2000) treebank acquired from the Penn Treebank (Marcus et al., 1993). This work is equally ap- plicable to the corpora described by Miyao et al. (2004), Shen et al. (2008) or Cahill et al. (2008). Our first changes integrate four previously sug- gested improvements to CCGbank. We then de- scribe a novel CCG analysis of NP predicate- argument structure, which we implement using NomBank (Meyers et al., 2004). Our analysis al- lows the distinction between core and peripheral arguments to be represented for predicate nouns. With this distinction, an entailment recognition system could recognise that Google’s acquisition of YouTube entailed Google acquired YouTube, be- cause equivalent predicate-argument structures are built for both. Our analysis also recovers non- local dependencies mediated by nominal predi- cates; for instance, Google is the agent of acquire in Google’s decision to acquire YouTube. The rebanked corpus extends CCGbank with: 1. NP brackets from Vadas and Curran (2008); 2. Restored and normalised punctuation; 3. Propbank-derived verb subcategorisation; 4. Verb particle structure drawn from Propbank; 5. Restrictive and non-restrictive …

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