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Der Beitrag beschreibt ein mehrfach annotiertes Korpus deutschsprachiger Songtexte als Datenbasis für interdisziplinäre Untersuchungsszenarien. Die Ressource erlaubt empirisch begründete Analysen sprachlicher Phänomene, systemischstruktureller Wechselbeziehungen und Tendenzen in den Texten moderner Popmusik. Vorgestellt werden Design und Annotationen des in thematische und autorenspezifische Archive stratifizierten Korpus sowie deskriptive Statistiken am Beispiel des Udo-Lindenberg-Archivs.
This contribution presents a quantitative approach to speech, thought and writing representation (ST&WR) and steps towards its automatic detection. Automatic detection is necessary for studying ST&WR in a large number of texts and thus identifying developments in form and usage over time and in different types of texts. The contribution summarizes results of a pilot study: First, it describes the manual annotation of a corpus of short narrative texts in relation to linguistic descriptions of ST&WR. Then, two different techniques of automatic detection – a rule-based and a machine learning approach – are described and compared. Evaluation of the results shows success with automatic detection, especially for direct and indirect ST&WR.
Formalisierung von Kontext und sprachlichem Wissen mit Prioritisierter Circumscription (VM-Memo 55)
(1994)
We present the annotation of information structure in the MULI project. To learn more about the information structuring means in prosody, syntax and discourse, theory- independent features were defined for each level. We describe the features and illustrate them on an example sentence. To investigate the interplay of features, the representation has to allow for inspecting all three layers at the same time. This is realised by a stand-off XML mark-up with the word as the basic unit. The theory-neutral XML stand-off annotation allows integrating this resource with other linguistic resources such as the Tiger Treebank for German or the Penn treebank for English.
We present an approach for modeling German negation in open-domain fine grained sentiment analysis. Unlike most previous work in sentiment analysis, we assume that negation can be conveyed by many lexical units (and not only common negation words) and that different negation words have different scopes. Our approach is examined on a new dataset comprising sentences with mentions of polar expressions and various negation words. We identify different types of negation words that have the same scopes. We show that already negation modeling based on these types largely outperforms traditional negation models which assume the same scope for all negation words and which employ a window-based scope detection rather than a scope detection based on syntactic information.
The goal of the MULI (MUltiLingual Information structure) project is to empirically analyse information structure in German and English newspaper texts. In contrast to other projects in which information structure is annotated and investigated (e.g. in the Prague Dependency Treebank, which mirrors the basic information about the topic-focus articulation of the sentence), we do not annotate theory-biased categories like topic-focus or theme-rheme. Trying to be as theory-independent as possible, we annotate those features which are relevant to information structure and on the basis of which typical patterns, co-occurrences or correlations can be determined. We distinguish between three annotation levels: syntax, discourse and prosody. The data is based on the TIGER Corpus for German and the Penn Treebank for English, since the existing information on part-of-speech and syntactic structure can be re-used for our purposes. The actual annotation of an English example sequence illustrates our choice of categories on each level. Their combination offers the possibility to investigate how information structure is realised and can be interpreted.