A robust and hybrid deep-linguistic theory applied to large-scale parsing

Modern statistical parsers are robust and quite fast, but their output is relatively shallow when compared to formal grammar parsers. We sug-gest to extend statistical approaches to a more deep-linguistic analysis while at the same time keeping the speed and low complexity of a sta-tistical parser. The resulting parsing architec-ture suggested, implemented and evaluated here is highly robust and hybrid on a number of levels, combining statistical and rule-based ap-proaches, constituency and dependency gram-mar, shallow and deep processing, full and near-full parsing. With its parsing speed of about 300,000 words per hour and state-of-the-art per-formance the parser is reliable for a number of large-scale applications discussed in the article