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In this paper, we investigate the practical applicability of Co-Training for the task of building a classifier for reference resolution. We are concerned with the question if Co-Training can significantly reduce the amount of manual labeling work and still produce a classifier with an acceptable performance.
We describe a simple and efficient Java object model and application programming interface (API) for (possibly multi-modal) annotated natural language corpora. Corpora are represented as elements like Sentences, Turns, Utterances, Words, Gestures and Markables. The API allows linguists to access corpora in terms of these discourse-level elements, i.e. at a conceptual level they are familiar with, with the flexibility offered by a general purpose programming language. It is also a contribution to corpus standardization efforts because it is based on a straightforward and easily extensible data model which can serve as a target for conversion of different corpus formats.
We present a light-weight tool for the annotation of linguistic data on multiple levels. It is based on the simplification of annotations to sets of markables having attributes and standing in certain relations to each other. We describe the main features of the tool, emphasizing its simplicity, customizability and versatility
Gesprächsprotokolle auf Knopfdruck: Die automatische Zusammenfassung von gesprochenen Dialogen
(2007)
Dieser Beitrag beschreibt computerlinguistische Arbeiten zur automatischen Zusammenfassung gesprochener Dialoge. Der Beitrag geht sowohl auf die notwendige Vorverarbeitung als auch auf die eigentliche Zusammenfassung durch automatische Erkennung von Themengrenzen und Extraktion relevanter Äußerungen ein. Ein weiterer Schwerpunkt liegt in der Beschreibung von Arbeiten zur automatischen Anaphernresolution in gesprochener Sprache. Der Beitrag betont vor allem die Rolle und Bedeutung von annotierten Korpora für die computerlinguistische Forschung und Entwicklung.
We apply a decision tree based approach to pronoun resolution in spoken dialogue. Our system deals with pronouns with NP- and non-NP-antecedents. We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features. We evaluate the system on twenty Switchboard dialogues and show that it compares well to Byron’s (2002) manually tuned system.