The paper describes two parsing schemes : a shallow approach based on machine learning and a cascaded finite-state parser with a hand-crafted grammar . It discusses several ways to combine them and presents evaluation results for the two individual approaches and their combination . An underspecification scheme for the output of the finite-state parser is introduced and shown to improve performance . In several areas of Natural Language Processing , a combination of different approaches has been found to give the best results . It is especially rewarding to combine deep and shallow systems , where the former guarantees interpretability and high precision and the latter provides robustness and high recall . This paper investigates such a combination consisting of an n-gram based shallow parser and a cascaded finite-state parser 1 with hand-crafted grammar and morphological checking . The respective strengths and weaknesses of these approaches are brought to light in an in-depth evaluation on a treebank of German newspaper texts ( Skut et al . , 1997 ) containing ca . 340,000 tokens in 19,546 sentences . The evaluation format chosen ( dependency tuples ) is used as the common denominator of the systems 1 Although not everyone would agree that finite-state parsers constitute a ' deep ' approach to parsing , they still are knowledge-based , require efforts of grammar-writing , a complex linguistic lexicon , manage without training data , etc . in building a hybrid parser with improved performance . An underspecification scheme allows the finite-state parser partially ambiguous output . It is shown that the other parser can in most cases successfully disambiguate such information . Section 2 discusses the evaluation format adopted ( dependency structures ) , its advantages , but also some of its controversial points . Section 3 formulates a classification problem on the basis of the evaluation format and applies a machine learner to it . Section 4 describes the architecture of the cascaded finite-state parser and its output in a novel underspecification format . Section 5 explores several combination strategies and tests them on several variants of the two base components . Section 6 provides an in-depth evaluation of the component systems and the hybrid parser . Section 7 concludes . The paper has presented two approaches to German parsing ( n-gram based machine learning and cascaded finite-state parsing ) , and evaluated them on the basis of a large amount of data . A new representation format has been introduced that allows underspecification of select types of syntactic ambiguity ( attachment and subcategorization ) even in the absence of a full-fledged chart . Several methods have been discussed for combining the two approaches . It has been shown that while combination with the shallow approach can only marginally improve performance of the cascaded parser if ideal disambiguation is assumed , a quite substantial rise is registered in situations closer to the real world where POS tagging is deficient and resolution of attachment and subcategorization ambiguities less than perfect . In ongoing work , we look at integrating a statistic context-free parser called BitPar , which was written by Helmut Schmid and achieves .816 F-score on NEGRA . Interestingly , the performance goes up to .9474 F-score when BitPar is combined with the FS parser ( upper bound ) and .9443 for the lower bound . So at least for German , combining parsers seems to be a pretty good idea . Thanks are due to Helmut Schmid and Prof. C. Rohrer for discussions , and to the reviewers for their detailed comments .
Combining different methods often achieves the best results especially combinations of shallow and deep can realize both interpretability and good results.
They propose several ways to combine a machine learning-based shallow method and a hand-crafted grammar-based cascaded method for parsers.
Evaluations on a treebank of German newspaper texts show that the proposed method achieves substantial gain when there are ambiguities.