A Cascaded Finite-State Parser for German

A Cascaded Finite-State Parser for German Michael Schiehlen Institute for Computational Linguistics, University of Stuttgart, Azenbergstr. 12, D-70174 Stuttgart mike@adler.ims.uni - stuttgart.de Abstract The paper presents two approaches to partial parsing of German: a tagger trained on dependency tuples, and a cas- caded finite-state parser (Abney, 1997). For the tagging approach, the effects of choosing different representations of de- pendency tuples are investigated. Per- formance of the finite-state parser is boosted by delaying syntactically un- solvable disambiguation problems via underspecification. Both approaches are evaluated on a 340,000-token corpus. 1 Introduction Traditional parsers are often quite brittle, and op- timize precision over recall. It is therefore impor- tant to also look at shallow approaches that come at virtually no cost in manual labour but can po- tentially supplement more knowledge-prone ap- proaches. The paper discusses one such approach which gets by with a tree bank and a tagger. An- other issue in parsing is speed, which can only be gained by deterministic processing. Determin- istic parsers return exactly one syntactic reading, which forces them to solve many locally unsolv- able puzzles. Abney (1997) suggests a way out of this dilemma: The parser leaves ambiguities unre- solved if they are contained in a local domain. So at least ambiguities of this kind can conceivably be handed over to some expert disambiguation mod- ule. The paper fleshes out this idea and shows its impact on overall performance. 2 Evaluation Method Instead of using the prevalent PARSEVAL mea- sures, we opted for a dependency-based evalua- tion (Lin, 1995), which is arguably (Srinivas et al., 1996) (Kiibler and Telljohann, 2002) fairer to partial parsers. In a dependency structure, every word token (dependent) is related to another token (head) over a grammatical role, but for one word token, which is called the root. Thus, a parser constructing a dependency structure needs to as- sociate every word token either with a head to- ken plus grammatical role or mark it as the root or 'TOP' node. The task can be seen as a classifica- tion problem and measured in (labelled) precision and recall. To simplify the task, grammatical roles can be neglected (unlabelled precision and recall). The details deserve some attention. With KOler and Telljohann (2002) and in contrast to Lin (1995), we assume that PPs are headed by their internal NPs, and that conjoined phrases have multiple heads (the conjuncts), with the conjunction linked to the last con- junct. Carroll et al. (1998) introduce additional links for control phenomena, map several to- kens to one node (e g linked preposition—noun and determiner—noun pairs), and allow nodes for elided words (e.g. in pro-/topic-drop and gap- ping). An important objection is that the weight of words is determined quite arbitrarily (Clark and Hockenmaier, 2002). Thus, we adopt Lin's scheme with the above provisos. Training and test sets for the experiments de- scribed below were derived from a tokenized ver- sion of the Negra tree bank of German newspaper 163 texts (Skut et al., 1997), comprising ca. 340,000 tokens …

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