Browsing by Author "Lapshinova-Koltunski, Ekaterina"

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    German clause-embedding predicates : an extraction and classification approach
    (2010) Lapshinova-Koltunski, Ekaterina; Heid, Ulrich (Prof. Dr. phil. habil.)
    This thesis describes a semi-automatic approach to the analysis of subcategorisation properties of verbal, nominal and multiword predicates in German. We semi-automatically classify predicates according to their subcategorisation properties by means of extracting them from German corpora along with their complements. In this work, we concentrate exclusively on sentential complements, such as dass, ob and w-clauses, although our methods can be also applied for other complement types. Our aim is not only to extract and classify predicates but also to compare subcategorisation properties of morphologically related predicates, such as verbs and their nominalisations. It is usually assumed that subcategorisation properties of nominalisations are taken over from their underlying verbs. However, our tests show that there exist different types of relations between them. Thus, we review subcategorisation properties of morphologically related words and analyse their correspondences and differences. For this purpose, we elaborate a set of semi-automatic procedures, which allow us not only to classify extracted units according to their subcategorisation properties, but also to compare the properties of verbs and their nominalisations, which occur both freely in corpora and within a multiword expression. The lexical data are created to serve symbolic NLP, especially large symbolic grammars for deep processing, such as HPSG or LFG, cf. work in the LinGO project (Copestake et al. 2004) and the Pargram project (Butt et al. 2002). HPSG and LFG need detailed linguistic knowledge. Besides that, subcategorisation iformation can be applied in applications for IE, cf. (Surdeanu et al. 2003). Moreover, this information is necessary for linguistic, lexicographic, SLA and translation work. Our extraction and classification procedures are precision-oriented, which means that we focus on high accuracy of our extraction and classification results. High precision is opposed to completeness, which is compensated by the application of extraction procedures on larger corpora.