Publications

Abstract

In Dutch and German, the canonical order of subject, object(s) and finite verb is ‘verb-second’ (V2) in main but ‘verb-final’ (VF) in subordinate clauses. This occasionally leads to the production of noncanonical word orders. Familiar examples are causative and argumentative clauses introduced by a subordinating conjunction (Du. omdat, Ger. weil ‘because’): the omdat/weil-V2 phenomenon. Such clauses may also be introduced by coordinating conjunctions (Du. want, Ger. denn), which license V2 exclusively. However, want/denn-VF structures are unknown. We present the results of a corpus study on the incidence of omdat-V2 in spoken Dutch, and compare them to published data on weil-V2 in spoken German. Basic findings: omdat-V2 is much less frequent than weil-V2 (ratio almost 1:8); and the frequency relations between coordinating and subordinating conjunctions are opposite (want >> omdat; denn << weil). We propose that conjunction selection and V2/VF selection proceed partly independently, and sometimes miscommunicate—e.g. yielding omdat/weil paired with V2. Want/denn-VF pairs do not occur because want/denn clauses are planned as autonomous sentences, which take V2 by default. We sketch a simple feedforward neural network with two layers of nodes (representing conjunctions and word orders, respectively) that can simulate the observed data pattern through inhibition-based competition of the alternative choices within the node layers.

Abstract

We outline an algorithm capable of generating varied but natural sounding sequences of argument NPs in subordinate clauses of German, a semi-free word order language. In order to attain the right level of output flexibility, the algorithm considers (1) the relevant lexical properties of the head verb (not only transitivity type but also reflexivity, thematic relations expressed by the NPs, etc.), and (2) the animacy and definiteness values of the arguments, and their length. The relevant statistical data were extracted from the NEGRA–II treebank and from hand-coded features for animacy and definiteness. The algorithm maps the relevant properties onto “primary” versus “secondary” placement options in the generator. The algorithm is restricted in that it does not take into account linear order determinants related to the sentence’s information structure and its discourse context (e.g. contrastiveness). These factors may modulate the above preferences or license “tertiary” linear orders beyond the primary and secondary options considered here.