Assigning Grammatical Relations with a Back-off Model

Assigning Grammatical Relations with a Back-off Model Erika F. de Lima GMD - German National Research Center for Information Technology Dolivostrasse 15 64293 Darmstadt, Germany delima@darmstadt, gmd. de Abstract This paper presents a corpus-based method to assign grammatical subject/object re- lations to ambiguous German constructs. It makes use of an unsupervised learning procedure to collect training and test data, and the back-off model to make assignment decisions. 1 Introduction Assigning a parse structure to the German sentence (1) involves addressing the fact that it is syntacti- cally ambiguous: (1) Eine hohe Inflationsrate erwartet die ()konomin. a high inflation rate expects the economist 'The economist expects a high inflation rate.' In this sentence it must be determined which nom- inal phrase is the subject of the verb. The verb er- warren ('to expect') takes, in one reading, a nom- inative NP as its subject and an accusative NP as its object. The nominal phrases preceding and fol- lowing the verb in (1) are both ambiguous with respect to case; they may be nominative or ac- cusative. Further, both NPs agree in number with the verb, and since in German any major con- stituent may be fronted in a verb-second clause, both NPs may be the subject/object of the verb. In this example, morpho-syntactical information is not sufficient to determine that the nominal phrase [NP die C)konomin] ('the economist') is the subject of the verb, and [NP Eine hohe Inflationsrate] ('a high inflation rate') its object. Determining the subject/object of an ambiguous construct such as (1) with a knowledge-based ap- proach requires (at least) a lexical representation specifying the classes of entities which may serve as arguments in the relation(s) denoted by each verb in the vocabulary, as well as membership informa- tion with respect to these classes for all entities de- noted by nouns in the vocabulary. One problem with this approach is that it is usually not available for a broad-coverage system. This paper proposes an approximation, similar to the empirical approaches to PP attachment decision (Hindle and Rooth, 1993; Ratnaparkhi, Reynar, and Roukos, 1994; Collins and Brooks, 1995). These make use of unambiguous examples provided by a treebank or a learning procedure in order to train a model to decide the attachment of ambiguous con- structs. In the current setting, this approach in- volves learning the classes of nouns occurring unam- biguously as subject/object of a verb in sample text, and using the classes thus obtained to disambiguate ambiguous constructs. Unambiguous examples are provided by sentences in which morpho-syntactical information suffices to determine the subject and object of the verb. For in- stance in (2), the nominal phrase [NP der C)konom] with a masculine head noun is unambiguously nom- inative, identifying it as the subject of the verb. In (3), both NPs are ambiguous with respect to case; however, the nominal phrase [NP Die 0konomen] with a plural head noun is the only one to agree in number with the verb, identifying it as its subject. (2) Eine hohe …

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