Parser lexicalisation through self-learning

Marek Rei and Ted Briscoe

We describe a new self-learning framework for parser lexicalisation that requires only a plain-text corpus of in-domain text. The method first creates augmented versions of dependency graphs by applying a series of modifications designed to directly capture higher-order lexical path dependencies. Scores are assigned to each edge in the graph using statistics from an automatically parsed background corpus. As bilexical dependencies are sparse, a novel directed distributional word similarity measure is used to smooth edge score estimates. Edge scores are then combined into graph scores and used for reranking the top-$n$ analyses found by the unlexicalised parser. The approach achieves significant improvements on WSJ and biomedical text over the unlexicalised baseline parser, which is originally trained on a subset of the Brown corpus.

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