Argument Differentiation

The ability to distinguish between different types of arguments is central to syntactic analysis, whether studied from a theoretical or computational point of view. This thesis investigates the influence and interaction of linguistic properties of syntactic arguments in argument differentiation. Cross-linguistic generalizations regarding these properties often express probabilistic, or soft, constraints, rather than absolute requirements on syntactic structure. In language data, we observe frequency effects in the realization of syntactic arguments. We propose that argument differentiation can be studied using data-driven methods which directly express the relationship between frequency distributions in language data and linguistic categories. The main focus in this thesis is on the formulation and empirical evaluation of linguistically motivated features for data-driven modeling. Based on differential properties of syntactic arguments in Scandinavian language data, we investigate the linguistic factors involved in argument differentiation from two different perspectives. We study automatic acquisition of the lexical semantic category of animacy and show that statistical tendencies in argument differentiation supports automatic classification of unseen nouns. The classification is furthermore robust, generalizable across machine learning algorithms, as well as scalable to larger data sets. We go on to perform a detailed study of the influence of a range of different linguistic properties, such as animacy, definiteness and finiteness, on argument disambiguation in data-driven dependency parsing of Swedish. By including features capturing these properties in the representations used by the parser, we are able to improve accuracy significantly, and in particular for the analysis of syntactic arguments. The thesis shows how the study of soft constraints and gradience in language can be carried out using data-driven models and argues that these provide a controlled setting where different factors may be evaluated and their influence quantified. By focusing on empirical evaluation, we come to a better understanding of the results and implications of the data-driven models and furthermore show how linguistic motivation in turn can lead to improved computational models.