Refine
Year of publication
Document Type
- Part of a Book (179)
- Article (166)
- Conference Proceeding (38)
- Review (4)
- Book (2)
- Other (2)
- Preprint (1)
Language
Keywords
- Deutsch (138)
- Korpus <Linguistik> (51)
- Konversationsanalyse (48)
- Interaktion (34)
- Semantik (23)
- Computerlinguistik (22)
- Kommunikation (21)
- Mehrsprachigkeit (21)
- Sprachpolitik (19)
- Englisch (18)
Publicationstate
- Postprint (392) (remove)
Reviewstate
- (Verlags)-Lektorat (172)
- Peer-Review (167)
- Peer-review (10)
- Verlags-Lektorat (5)
- Peer-Revied (2)
- (Verlags-)Lektorat (1)
- Review-Status-unbekannt (1)
- Zweitveröffentlichung (1)
Publisher
- Benjamins (52)
- Springer (36)
- Oxford University Press (19)
- Elsevier (14)
- Wilhelm Fink (14)
- Erich Schmidt (11)
- Buske (10)
- Winter (8)
- Equinox (6)
- Lang (6)
The paper investigates the evolution of document grammars from a linguistic point of view. Document grammars have been developed in the past decades in order to formalize knowledge on the structure of textual information. A well-known instance of a document grammar is the »Document Type Definition« (DTD) as part of the Extensible Markup Language (XML). DTDs allow to define so-called tree grammars that constrain the application of tag-sets in the process of annotation of a document. In an XML-based document workflow, DTDs play a crucial role for validation and transforming huge amounts of texts in standardized data formats. An interesting point in the development of XML DTDs is the fact that the restriction of the formal expressiveness paved the way to understand the formal properties of document grammars better and to develop more a powerful version like XML Schema recently. In this sense, the simplicity of the original approach, resulting from the necessary restriction of previous approaches, yielded new complexity on formally understood grounds.
In dependenzsyntaktischen Systemen wie denen von Engel (1982), Hudson (1984), Schubert (1987), Mel'čuk (1988) oder Starosta (1988) können gemeinhin nur Wörter andere Wörter oder Phrasen regieren. Auch wenn diese Annahme durchaus praktikabel ist, führt sie doch zu einer ganzen Reihe von syntaxtheoretischen Unzulänglichkeiten, die ausgearbeitete Dependenzgrammatiken gegenüber konkurrierenden Grammatiktheorien als unzulänglich erscheinen lassen. Ziel des vorliegenden Beitrages ist es, die Notwendigkeit darzulegen, auch komplexeren Einheiten Rektionsfähigkeit zuzugestehen, und mit dem Konzept des 'komplexen Elements' ein geeignetes formales Instrument dafür zur Verfügung zu stellen.
Situiertheit
(1993)
Discourse parsing of complex text types such as scientific research articles requires the analysis of an input document on linguistic and structural levels that go beyond traditionally employed lexical discourse markers. This chapter describes a text-technological approach to discourse parsing. Discourse parsing with the aim of providing a discourse structure is seen as the addition of a new annotation layer for input documents marked up on several linguistic annotation levels. The discourse parser generates discourse structures according to the Rhetorical Structure Theory. An overview of the knowledge sources and components for parsing scientific joumal articles is given. The parser’s core consists of cascaded applications of the GAP, a Generic Annotation Parser. Details of the chart parsing algorithm are provided, as well as a short evaluation in terms of comparisons with reference annotations from our corpus and with recently developed Systems with a similar task.
Dieser Beitrag skizziert die Möglichkeiten, die die Extensible Markup Language (XML) im Umfeld von eLearning und Web Based Training (WBT) eröffnet. Bisherige eLearning-Angebote kranken an verschiedenen Problemen, die durch die Verwendung von XML-basierten Learning Objects vermieden werden können. Ausgehend vom aktuellen Stand im Projekt MiLCA - Medienintensive Lehrmodule in der Computerlinguistik-Ausbildung - soll zudem ein Ausblick auf zukünftige technische Möglichkeiten des Computer-gestützten Lernens gegeben werden.
This chapter addresses the requirements and linguistic foundations of automatic relational discourse analysis of complex text types such as scientific journal articles. It is argued that besides lexical and grammatical discourse markers, which have traditionally been employed in discourse parsing, cues derived from the logical and generical document structure and the thematic structure of a text must be taken into account. An approach to modelling such types of linguistic information in terms of XML-based multi-layer annotations and to a text-technological representation of additional knowledge sources is presented. By means of quantitative and qualitative corpus analyses, cues and constraints for automatic discourse analysis can be derived. Furthermore, the proposed representations are used as the input sources for discourse parsing. A short overview of the projected parsing architecture is given.
Discourse segmentation is the division of a text into minimal discourse segments, which form the leaves in the trees that are used to represent discourse structures. A definition of elementary discourse segments in German is provided by adapting widely used segmentation principles for English minimal units, while considering punctuation, morphology, sytax, and aspects of the logical document structure of a complex text type, namely scientific articles. The algorithm and implementation of a discourse segmenter based on these principles is presented, as well an evaluation of test runs.
HMMs are the dominating technique used in speech recognition today since they perform well in overall phone recognition. In this paper, we show the comparison of HMM methods and machine learning techniques, such as neural networks, decision trees and ensemble classifiers with boosting and bagging in the task of articulatory-acoustic feature classification. The experimental results show that HMM methods work well for the classification of such features as vocalic. However, decision tree and bagging outperform HMMs for the fricative classification task since the data skewness is much higher than for the feature vocalic classification task. This demonstrates that HMMs do not perform as well as decision trees and bagging in highly skewed data settings.