Large-Scale Supervised Models for Noun Phrase Bracketing

Interpreting the structure of noun phrases (NPs) is important for many Natural Language Processing (NLP) tasks. This work extends the state-of-the-art in NP bracketing by: creating supervised models trained on a large annotated corpus; applying these to longer, more complex NPs; and using the resulting system to improve the output of the Bikel (2004) parser. Using a large corpus of manually annotated Penn Treebank NPs we have developed a supervised model that brackets simple NPs with 93.01% F-score. We extend the evaluation to include longer, more complex NPs that are rarely dealt with in the literature, attaining 91.44% F-score. Finally, we implement a post-processing module that brackets NPs identified by the Bikel (2004) parser, which outperforms the parser itself by 8.13% F-score.

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