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Analysing biomedical text with the Stanford dependency grammar

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If you have a question about this talk, please contact Johanna Geiss .

The Stanford parser and natural language tools come with a dependency grammar and a deterministic algorithm for mapping from standard Penn Treebank-style parse trees to dependency graphs. We have shown how this provides a lingua franca for comparing various statistical parsers on biomedical text, using benchmarking methods that highlight deficiencies in their behaviour much better than tree-based analyses. We also believe dependency graphs offer a tractable intermediate step between syntax and semantics and have developed some simple approaches to information extraction based on graph traversal.

This talk is part of the NLIP Seminar Series series.

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