A low-complexity, broad-coverage probabilistic dependency parser for English

Abstract

Large-scale parsing is still a complex and timeconsuming process, often so much that it is infeasible in real-world applications. The parsing system described here addresses this problem by combining finite-state approaches, statistical parsing techniques and engineering knowledge, thus keeping parsing complexity as low as possible at the cost of a slight decrease in performance. The parser is robust and fast and at the same time based on strong linguistic foundations.

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

“…Hybridness means that the most robust approach can be chosen for each task and each processing level. statistical vs. rule-based the most obvious way in which Pro3Gres is a hybrid (Schneider, 2003b). Unlike formal grammars to which posthoc statistical disambiguators can be added, Pro3Gres has been designed to be hybrid, carefully distinguishing between tasks that can best be solved by finite-state methods, rule-based methods and statistical methods.…”

Towards a semantic classification of Spanish verbs based on subcategorisation information

“…Knowledge about verbs is especially important, since verbs are the primary means of structuring and conveying meaning in sentences. Manually built semantic classifications of English verbs have been used for different applications such as machine translation (Dorr, 1997), verb subcategorisation acquisition (Korhonen, 2002a) or parsing (Schneider, 2003). (Levin, 1993) has established a large-scale classification of English verbs based on the hypothesis that the meaning of a verb and its syntactic behaviour are related, and therefore semantic information can be induced from the syntactic behaviour of the verb.…”