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Accurate dependency recovery has recently been reported for a number of wide-coverage statistical parsers using Combinatory Categorial Grammar (CCG). However, overall figures give no indication of a parser’s performance on …...

Efficient wide-coverage parsing is integral to large-scale NLP applications. Unfortu- nately, parsers for linguistically motivated formalisms, e.g. HPSG and TAG, are often too inefficient for these applications. This paper describes …...

In this paper we investigate the benefit of stochastic predictor components for the parsing quality which can be obtained with a rule-based dependency grammar. By in- cluding a chunker, a …...

Parsers are often the bottleneck for data ac- quisition, processing text too slowly to be widely applied. One way to improve the efficiency of parsers is to construct more confident …...

We propose a novel self-training method for a parser which uses a lexicalised gram- mar and supertagger, focusing on increas- ing the speed of the parser rather than its accuracy. …...

We combine two complementary ideas for learning supertaggers from highly am- biguous lexicons: grammar-informed tag transitions and models minimized via in- teger programming. Each strategy on its own greatly improves …...