Publication: A robust and hybrid deep-linguistic theory applied to large scale parsing
A robust and hybrid deep-linguistic theory applied to large scale parsing
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Schneider, G., Rinaldi, F., & Dowdall, J. (2004). A robust and hybrid deep-linguistic theory applied to large scale parsing. 14–23.
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Modern statistical parsers are robust and quite fast, but their output is relatively shallow when compared to formal grammar parsers. We suggest to extend statistical approaches to a more deep-linguistic analysis while at the same time keeping the speed and low complexity of a statistical parser. The resulting parsing architecture suggested, implemented and evaluated here is highly robust and hybrid on a number of levels, combining statistical and rule-based approaches, constituency and dependency grammar, shallow and deep processing,
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Schneider, G., Rinaldi, F., & Dowdall, J. (2004). A robust and hybrid deep-linguistic theory applied to large scale parsing. 14–23.
