Abstract. Discourse Segmentation is the division o f a text into mini mal 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 Segmenta tion principles for Englisli minimal units, wlnle considenng punctuation, morphology, sytax, and aspects of the logical document structure of a complex text type, namely scientific articles. The algorithm and implei nentatic .n of a discourse segmenter based on these principles is presented, as well an evaluation of test runs.

This article describes an investigation of the relationship between anaphora and relational discourse structure, notably the Elaboration relation known from theories like RST. A corpus was annotated on the levels of anaphoric structure and rhetorical structure. The statistical analysis of interrelations between the two annotation layers revealed correlations between specific subtypes of anaphora and Elaboration, indicating that anaphora can function as a cue for Elaboration.   The work presented in this article is a joint effort of the projects A (Sekimo) and C (SemDok ) of the Research Group  Text-technological modelling of information funded by the German Research Foundation DFG.

Preface E-Learning is more and more becoming a commodity in all areas of education ranging from curricular learning at schools and universities via further education and learning on the job to social and game-based learning in spare time activities. At the same time, research on E-Learning is scattered across different research disciplines, being often a matter of single initiatives and persons. In the light of this growing gap, the authors of this introduction want to showcase Computational Linguistics and Language Technology as possible key enabling technologies for current E-Learning. 1Current trends in E-Learning E-Learning, or Technology-Enhanced Learning is an interdisciplinary research area which involves research disciplines like Pedagogy, Pedagogical Psychology, Computer Science (particularly research on human-computer interaction (HCI), knowledge management, and software architectures for virtual learning management systems), Artificial Intelligence (especially research on intelligent tutoring systems (ITS)) and Computational Linguistics (in particular computer-assisted language learning, educational natural language processing and text technology for learning technology standards and applications). In the last 15 years, the research area "E-Learning" has undergone major changes. While the first decade, starting in the end of the 20th century, has mainly focused on learning infrastructure and learning content, more recently, the learning process and the learners have been taken more seriously. Today, a learner-centred paradigm dominates E-Learning. Current areas of interest are adaptivity and personalization (individual and ubiquitous learning settings, personal infrastructures supporting life long learning), learning in social communities, and learning & entertainment (game-based learning). Taking up trends in the Web 2.0, attention has been given to aspects like virtual learning communities, web based collaboration, social media content and social learning infrastructures (e.g. edublogs and wikis). This insight also had an influence in research on and development of virtual learning environments: Personal learning environments or mashup personal learning environments are subjects of increasing interest, and are more and more seen as more adequate promoters of life long learning than traditional virtual learning environments. Moreover, applications and methods supporting learning at any time and any place are also seen as important factors to improve life long learning. Catch phrases in this context are "ubiquitous learning", "mobile learning" and "augmented reality". It can be expected that these research and development fields will become more and more important in the next years. Beside these trends, two more important aspects of current technology-enhanced learning have to be mentioned: E-assessment and game-based learning. E-Assessment has always played a major role in E-Learning, but has long been restricted to simple multiple-

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. Keywords Discourse parsing • Discourse relations • Document structure • Text technology • Linguistic annotations • XML IntroductionIn the past, several approaches to automatic discourse analysis have been developed as applications of relational discourse theories which describe the semantics of discourse. These approaches are often based on the analysis of discourse connectives as well as morphological and syntactic features. Such surface-oriented strategies are adequate and have yielded good results when applied to the analysis of simple text types like newspaper articles, which are characterised by a limited size and a relatively simple document and syntactic structure. When dealing with more complex text types, however, an analysis of lexis and grammar is not sufficient. Sources of knowledge about discourse and document semantics have to be considered as well.H. Lüngen (m ) Justus-Liebig-Universität Gießen. Gießen, Germany e-mail: luengen@uni-giessen. de Published in: Witt, Andreas/Metzing, Dieter (eds.): Linguistic Modeling of Information and Markup Languages. Contributions to Language Technology. -Dordrecht: Springer, 2010. pp. 97-123. (Text, Speech and Language Technology 41) This chapter deals with the linguistic foundations of discourse analysis for a complex text type by the example of scientific journal articles. Its focus is on the contribution of logical document structure, generic document structure and thematic structure to discourse parsing. The modelling and representation of linguistic structures and knowledge sources based on text-technological (XML-based) formalisms and methods is addressed. The representations are used in investigating correlations and interactions between different types of linguistic information and serve as an input to a discourse parsing System.In the project SemDok, which is part of the Research Group Text-technological modelling o f information funded by the German Research Foundation DFG and scheduled to run in its second phase for three years 2005-2008, a discourse parser for the complex text type "scientific research article" is being developed. Scientific ...

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