Language Independent Probabilistic Context-Free Parsing Bolstered by Machine Learning

Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL-X), pages 231–235, New York City, June 2006. c⃝2006 Association for Computational Linguistics Language Independent Probabilistic Context-Free Parsing Bolstered by Machine Learning Michael Schiehlen Kristina Spranger Institute for Computational Linguistics University of Stuttgart D-70174 Stuttgart Michael.Schiehlen@ims.uni-stuttgart.de Kristina.Spranger@ims.uni-stuttgart.de Abstract Unlexicalized probabilistic context-free parsing is a general and flexible approach that sometimes reaches competitive re- sults in multilingual dependency parsing even if a minimum of language-specific information is supplied. Furthermore, in- tegrating parser results (good at long de- pendencies) and tagger results (good at short range dependencies, and more easily adaptable to treebank peculiarities) gives competitive results in all languages. 1 Introduction Unlexicalized probabilistic context-free parsing is a simple and flexible approach that nevertheless has shown good performance (Klein and Manning, 2003). We applied this approach to the shared task (Buchholz et al., 2006) for Arabic (Hajiˇc et al., 2004), Chinese (Chen et al., 2003), Czech (Böh- mová et al., 2003), Danish (Kromann, 2003), Dutch (van der Beek et al., 2002), German (Brants et al., 2002), Japanese (Kawata and Bartels, 2000), Por- tuguese (Afonso et al., 2002), Slovene (Džeroski et al., 2006), Spanish (Civit Torruella and Martí An- tonín, 2002), Swedish (Nilsson et al., 2005), Turk- ish (Oflazer et al., 2003; Atalay et al., 2003), but not Bulgarian (Simov et al., 2005). In our ap- proach we put special emphasis on language inde- pendence: We did not use any extraneous knowl- edge; we did not do any transformations on the treebanks; we restricted language-specific parame- ters to a small, easily manageable set (a classifica- tion of dependency relations into complements, ad- juncts, and conjuncts/coordinators, and a switch for Japanese to include coarse POS tag information, see section 3.4). In a series of post-submission experi- ments, we investigated how much the parse results can help a machine learner. 2 Experimental Setup For development, we chose the initial � sentences of every treebank, where � is the number of the sen- tences in the test set. In this way, the sizes were realistic for the task. For parsing the test data, we added the development set to the training set. All the evaluations on the test sets were performed with the evaluation script supplied by the conference organizers. For development, we used labelled F- score computed from all tokens except the ones em- ployed for punctuation (cf. section 3.2). 3 Context Free Parsing 3.1 The Parser Basically, we investigated the performance of a straightforward unlexicalized statistical parser, viz. BitPar (Schmid, 2004). BitPar is a CKY parser that uses bit vectors for efficient representation of the chart and its items. If frequencies for the grammat- ical and lexical rules in a training set are available, BitPar uses the Viterbi algorithm to extract the most probable parse tree (according to PCFG) from the chart. 231 3.2 Converting Dependency Structure to Constituency Structure In order to determine the grammar rules required by the context-free parser, the dependency trees in the CONLL format have to be converted to …

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