Using MONA for Querying Linguistic Treebanks

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 555–562, Vancouver, October 2005. c⃝2005 Association for Computational Linguistics Using MONA for Querying Linguistic Treebanks Stephan Kepser∗ Collaborative Research Centre 441 University of T¨ubingen T¨ubingen, Germany kepser@sfs.uni-tuebingen.de Abstract MONA is an automata toolkit provid- ing a compiler for compiling formulae of monadic second order logic on strings or trees into string automata or tree automata. In this paper, we evaluate the option of using MONA as a treebank query tool. Unfortunately, we find that MONA is not an option. There are several reasons why the main being unsustainable query an- swer times. If the treebank contains larger trees with more than 100 nodes, then even the processing of simple queries may take hours. 1 Introduction In recent years large amounts of electronic texts have become available providing a new base for empiri- cal studies in linguistics and offering a chance to lin- guists to compare their theories with large amounts of utterances from “the real world”. While tagging with morphosyntactic categories has become a stan- dard for almost all corpora, more and more of them are nowadays annotated with refined syntactic in- formation. Examples are the Penn Treebank (Mar- cus et al., 1993) for American English annotated at the University of Pennsylvania, the French treebank (Abeill´e and Cl´ement, 1999) developed in Paris, the TIGER Corpus (Brants et al., 2002) for German annotated at the Universities of Saarbr¨ucken and ∗ This research was funded by a German Science Founda- tion grant (DFG SFB441-6). Stuttgart, and the T¨ubingen Treebanks (Hinrichs et al., 2000) for Japanese, German and English from the University of T¨ubingen. To make these rich syn- tactic annotations accessible for linguists, the devel- opment of powerful query tools is an obvious need and has become an important task in computational linguistics. Consequently, a number of treebank query tools have been developed. Probably amongst the most important ones are CorpusSearch (Randall, 2000), ICECUP III (Wallis and Nelson, 2000), fsq (Kepser, 2003), TGrep2 (Rohde, 2001), and TIGERSearch (K¨onig and Lezius, 2000). A common feature of these tools is the relatively low expressive power of their query languages. Explicit or implicit ref- erences to nodes in a tree are mostly interpreted ex- istentially. The notable exception is fsq, which em- ploys full first order logic as its query language. The importance of the expressive power of the query language is a consequence of the sizes of the available treebanks, which can contain several ten- thousand trees. It is clearly impossible to browse these treebanks manually searching for linguistic phenomena. But a query tool that does not permit the user to specify the sought linguistic phenomenon quite precisely is not too helpful, either. If the user can only approximate the phenomenon he seeks an- swer sets will be very big, often containing several hundred to thousand trees. Weeding through answer sets of this size is cumbersome and not really fruit- ful. If the task is to gain …

Content truncated to 500 words