Dependency Parsing of Hungarian: Baseline Results and Challenges

Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 55–65, Avignon, France, April 23 - 27 2012. c⃝2012 Association for Computational Linguistics Dependency Parsing of Hungarian: Baseline Results and Challenges Rich´ard Farkas1, Veronika Vincze2, Helmut Schmid1 1Institute for Natural Language Processing, University of Stuttgart {farkas,schmid}@ims.uni-stuttgart.de 2Research Group on Artificial Intelligence, Hungarian Academy of Sciences vinczev@inf.u-szeged.hu Abstract Hungarian is a stereotype of morpholog- ically rich and non-configurational lan- guages. Here, we introduce results on de- pendency parsing of Hungarian that em- ploy a 80K, multi-domain, fully manu- ally annotated corpus, the Szeged Depen- dency Treebank. We show that the results achieved by state-of-the-art data-driven parsers on Hungarian and English (which is at the other end of the configurational-non- configurational spectrum) are quite simi- lar to each other in terms of attachment scores. We reveal the reasons for this and present a systematic and comparative lin- guistically motivated error analysis on both languages. This analysis highlights that ad- dressing the language-specific phenomena is required for a further remarkable error re- duction. 1 Introduction From the viewpoint of syntactic parsing, the lan- guages of the world are usually categorized ac- cording to their level of configurationality. At one end, there is English, a strongly configurational language while Hungarian is at the other end of the spectrum. It has very few fixed structures at the sentence level. Leaving aside the issue of the internal structure of NPs, most sentence-level syntactic information in Hungarian is conveyed by morphology, not by configuration (´E. Kiss, 2002). A large part of the methodology for syntactic parsing has been developed for English. How- ever, parsing non-configurational and less config- urational languages requires different techniques. In this study, we present results on Hungarian de- pendency parsing and we investigate this general issue in the case of English and Hungarian. We employed three state-of-the-art data-driven parsers (Nivre et al., 2004; McDonald et al., 2005; Bohnet, 2010), which achieved (un)labeled at- tachment scores on Hungarian not so different from the corresponding English scores (and even higher on certain domains/subcorpora). Our in- vestigations show that the feature representation used by the data-driven parsers is so rich that they can – without any modification – effectively learn a reasonable model for non-configurational lan- guages as well. We also conducted a systematic and compar- ative error analysis of the system’s outputs for Hungarian and English. This analysis highlights the challenges of parsing Hungarian and sug- gests that the further improvement of parsers re- quires special handling of language-specific phe- nomena. We believe that some of our findings can be relevant for intermediate languages on the configurational-non-configurational spectrum. 2 Chief Characteristics of the Hungarian Morphosyntax Hungarian is an agglutinative language, which means that a word can have hundreds of word forms due to inflectional or derivational affixa- tion. A lot of grammatical information is encoded in morphology and Hungarian is a stereotype of morphologically rich languages. The Hungarian word order is free in the sense that the positions of …

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