From Chunks to Function-Argument Structure: A Similarity-Based Approach

From Chunks to Function-Argument Structure: A Similarity-Based Approach Sandra K¨ubler � and Erhard W. Hinrichs � � Seminar f¨ur Sprachwissenschaft University of T¨ubingen D-72074 T¨ubingen, Germany � kuebler,eh � @sfs.nphil.uni-tuebingen.de Abstract Chunk parsing has focused on the recognition of partial constituent struc- tures at the level of individual chunks. Little attention has been paid to the question of how such partial analyses can be combined into larger structures for complete utterances. Such larger structures are not only desirable for a deeper syntactic analysis. They also constitute a necessary prerequisite for assigning function-argument structure. The present paper offers a similarity- based algorithm for assigning func- tional labels such as subject, object, head, complement, etc. to complete syntactic structures on the basis of pre- chunked input. The evaluation of the algorithm has concentrated on measuring the quality of functional labels. It was performed on a German and an English treebank using two different annotation schemes at the level of function-argument struc- ture. The results of 89.73 % cor- rect functional labels for German and 90.40 % for English validate the general approach. 1 Introduction Current research on natural language parsing tends to gravitate toward one of two extremes: robust, partial parsing with the goal of broad data coverage versus more traditional parsers that aim at complete analysis for a narrowly defined set of data. Chunk parsing (Abney, 1991; Ab- ney, 1996) offers a particularly promising and by now widely used example of the former kind. The main insight that underlies the chunk pars- ing strategy is to isolate the (finite-state) analysis of non-recursive syntactic structure, i.e. chunks, from larger, recursive structures. This results in a highly-efficient parsing architecture that is realized as a cascade of finite-state transducers and that pursues a leftmost longest-match pattern- matching strategy at each level of analysis. Despite the popularity of the chunk parsing ap- proach, there seems to be a gap in current re- search: Chunk parsing research has focused on the recognition of partial constituent structures at the level of individual chunks. By comparison, lit- tle or no attention has been paid to the ques- tion of how such partial analyses can be com- bined into larger structures for complete utter- ances. Such larger structures are not only de- sirable for a deeper syntactic analysis; they also constitute a necessary prerequisite for assigning function-argument structure. Automatic assignment of function-argument structure has long been recognized as a desider- atum beyond pure syntactic labeling (Marcus et al., 1994)1. The present paper offers a similarity- 1With the exception of dependency-grammar-based parsers (Tapanainen and J¨arvinen, 1997; Br¨oker et al., 1994; Lesmo and Lombardo, 2000), where functional labels are treated as first-class citizens as relations between words, and recent work on a semi-automatic method for treebank con- struction (Brants et al., 1997), little has been reported on based algorithm for assigning functional labels such as subject, object, head, complement, etc. to complete syntactic structures on the basis of pre-chunked input. The evaluation of the algo- rithm has concentrated on measuring the …

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