Parsing Arguments of Nominalizations in English and Chinese

In this paper, we use a machine learning framework for semantic argument parsing, and apply it to the task of parsing arguments of eventive nominalizations in the FrameNet database. We create a baseline system using a subset of features introduced by Gildea and Jurafsky (2002), which are directly applicable to nominal predicates. We then investigate new features which are designed to capture the novelties in nominal argument structure and show a significant performance improvement using these new features. We also investigate the parsing performance of nominalizations in Chinese and compare the salience of the features for the two languages.

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