Kordula De Kuthy

On Partial Constituent Fronting in German

This paper presents a reevaluation of the choice between the two analyses for German partial fronting phenomena proposed in the literature, remnant movement and reanalysis. We show that the empirical arguments which were presented in favor of an extraction analysis are not convincing, and we provide empirical evidence supporting a reanalysis-like approach. Turning to a detailed data discussion, we compare three different kinds of partial constituents: verbal, adjectival, and nominal ones. Adjectival complements pattern with coherently selected verbal complements whereas nominal complements turn out to be less restricted. On the theoretical side, we show that a reanalysis-like theory can be given a formally precise rendering in the HPSG architecture in terms of a lexical argument-raising specification, which is already widely employed in HPSG analyses of coherence in Germanic and restructuring verbs in Romance languages. The account we propose generalizes previous HPSG approaches to partial complements of different categories and correctly predicts the interaction of (partial) VP topicalization with embedded partial NPs or APs.

Focus Annotation of Task-based Data: Establishing the Quality of Crowd Annotation.

We explore the annotation of information structure in German and compare the quality of expert annotation with crowdsourced annotation taking into account the cost of reaching crowd consensus. Concretely, we discuss a crowd-sourcing effort annotating focus in a task-based corpus of German containing reading comprehension questions and answers. Against the backdrop of a gold standard reference resulting from adjudicated expert annotation, we evaluate a crowd sourcing experiment using majority voting to determine a baseline performance. To refine the crowd-sourcing setup, we introduce the Consensus Cost as a measure of agreement within the crowd. We investigate the usefulness of Consensus Cost as a measure of crowd annotation quality both intrinsically, in relation to the expert gold standard, and extrinsically, by integrating focus annotation information into a system performing Short Answer Assessment taking into account the Consensus Cost. We find that low Consensus Cost in crowd sourcing indicates high quality, though high cost does not necessarily indicate low accuracy but increased variability. Overall, taking Consensus Cost into account improves both intrinsic and extrinsic evaluation measures.

Approximating Givenness in Content Assessment through Distributional Semantics

Givenness (Schwarzschild, 1999) is one of the central notions in the formal pragmatic literature discussing the organization of discourse. In this paper, we explore where distributional semantics can help address the gap between the linguistic insights into the formal pragmatic notion of Givenness and its implementation in computational linguistics. As experimental testbed, we focus on short answer assessment, in which the goal is to assess whether a student response correctly answers the provided reading comprehension question or not. Current approaches only implement a very basic, surface-based perspective on Givenness: A word of the answer that appears as such in the question counts as GIVEN. We show that an approach approximating Givenness using distributional semantics to check whether a word in a sentence is similar enough to a word in the context to count as GIVEN is more successful quantitatively and supports interesting qualitative insights into the data and the limitations of a basic distributional semantic approach identifying Givenness at the lexical level.