A Statistical Parser for Czech

A Statistical Parser for Czech* Michael Collins AT&T Labs-Research, Shannon Laboratory, 180 Park Avenue, Florham Park, NJ 07932 mcollins@research, att.com Jan Haj i~. Institute of Formal and Applied Linguistics Charles University, Prague, Czech Republic hajic@ufal.mff, cuni. cz Lance Ramshaw BBN Technologies, 70 Fawcett St., Cambridge, MA 02138 i r amshaw@bbn, c om Christoph Tillmann Lehrstuhl ftir Informatik VI, RWTH Aachen D-52056 Aachen, Germany tillmann@informatik, rwth-aachen, de Abstract This paper considers statistical parsing of Czech, which differs radically from English in at least two respects: (1) it is a highly inflected language, and (2) it has relatively free word order. These dif- ferences are likely to pose new problems for tech- niques that have been developed on English. We describe our experience in building on the parsing model of (Collins 97). Our final results - 80% de- pendency accuracy - represent good progress to- wards the 91% accuracy of the parser on English (Wall Street Journal) text. 1 Introduction Much of the recent research on statistical parsing has focused on English; languages other than En- glish are likely to pose new problems for statisti- cal methods. This paper considers statistical pars- ing of Czech, using the Prague Dependency Tree- bank (PDT) (Haji~, 1998) as a source of training and test data (the PDT contains around 480,000 words of general news, business news, and science articles * This material is based upon work supported by the National Science Foundation under Grant No. (#IIS-9732388), and was carded out at the 1998 Workshop on Language Engineering, Center for Language and Speech Processing, Johns Hopkins University. Any opinions, findings, and conclusions or recom- mendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Sci- ence Foundation or The Johns Hopkins University. The project has also had support at various levels from the following grants and programs: Grant Agency of the Czech Republic grants No. 405/96/0198 and 405/96/K214 and Ministry of Education of the Czech Republic Project No. VS96151. We would also like to thank Eric Brill, Barbora Hladk~i, Frederick Jelinek, Doug Jones, Cynthia Kuo, Oren Schwartz, and Daniel Zeman for many useful discussions during and after the workshop. annotated for dependency structure). Czech differs radically from English in at least two respects: • It is a highly inflected (HI) language. Words in Czech can inflect for a number of syntac- tic features: case, number, gender, negation and so on. This leads to a very large number of possible word forms, and consequent sparse data problems when parameters are associated with lexical items, on the positive side, inflec- tional information should provide strong cues to parse structure; an important question is how to parameterize a statistical parsing model in a way that makes good use of inflectional infor- mation. • It has relatively free word order (F-WO). For example, a subject-verb-object triple in Czech can generally appear in all 6 possible surface orders (SVO, SOV, VSO etc.). Other Slavic languages (such as Polish, Russian, Slovak, Slovene, …

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