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Automatic recognition of speech, thought, and writing representation in German narrative texts
(2013)
This article presents the main results of a project, which explored ways to recognize and classify a narrative feature—speech, thought, and writing representation (ST&WR)—automatically, using surface information and methods of computational linguistics. The task was to detect and distinguish four types—direct, free indirect, indirect, and reported ST&WR—in a corpus of manually annotated German narrative texts. Rule-based as well as machine-learning methods were tested and compared. The results were best for recognizing direct ST&WR (best F1 score: 0.87), followed by indirect (0.71), reported (0.58), and finally free indirect ST&WR (0.40). The rule-based approach worked best for ST&WR types with clear patterns, like indirect and marked direct ST&WR, and often gave the most accurate results. Machine learning was most successful for types without clear indicators, like free indirect ST&WR, and proved more stable. When looking at the percentage of ST&WR in a text, the results of machine-learning methods always correlated best with the results of manual annotation. Creating a union or intersection of the results of the two approaches did not lead to striking improvements. A stricter definition of ST&WR, which excluded borderline cases, made the task harder and led to worse results for both approaches.
In this paper we present an evaluation of rule-based morphological components for German for use in an interactive editing environment. The criteria for the evaluation are deduced from the intended use of these components, namely availability, performance, programming interfaces, and analysis quality. We evaluated systems developed and maintained since decades as well as new systems. However, we note serious general shortcomings when looking closer at recent implementations and come to the conclusion that the oldest system is the only one that satisfies our requirements.
The authors establish a phenomenological perspective on the temporal constitution of experience and action. Retrospection and projection (i.e. backward as well as forward orientation of everyday action), sequentiality and the sequential organization of activities as well as simultaneity (i.e. participants’ simultaneous coordination) are introduced as key concepts of a temporalized approach to interaction. These concepts are used to capture that every action is produced as an inter-linked step in the succession of adjacent actions, being sensitive to the precise moment where it is produced. The adoption of a holistic, multimodal and praxeological perspective additionally shows that action in interaction is organized according to several temporal orders simultaneously in operation. Each multimodal resource used in interaction has its own temporal properties.
This paper shows how understanding in interaction is informed by temporality, and in particular, by the workings of retrospection. Understanding is a temporally extended, sequentially organized process. Temporality, namely, the sequential relationship of turn positions, equips participants with default mechanisms to display understandings and to expect such displays. These mechanisms require local management of turn-taking to be in order, i.e., the possibility and the expectation to respond locally and reciprocally to prior turns at talk. Sequential positions of turns in interaction provide an infrastructure for displaying understanding and accomplishing intersubjectivity. Linguistic practices specialized in displaying particular kinds of (not) understanding are adapted to the individual sequential positions with respect to an action-to-be-understood.