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We present an approach on how to investigate what kind of semantic information is regularly associated with the structural markup of scientific articles. This approach addresses the need for an explicit formal description of the semantics of text-oriented XML-documents. The domain of our investigation is a corpus of scientific articles from psychology and linguistics from both English and German online available journals. For our analyses, we provide XML-markup representing two kinds of semantic levels: the thematic level (i.e. topics in the text world that the article is about) and the functional or rhetorical level. Our hypothesis is that these semantic levels correlate with the articles’ document structure also represented in XML. Articles have been annotated with the appropriate information. Each of the three informational levels is modelled in a separate XML document, since in our domain, the different description levels might conflict so that it is impossible to model them within a single XML document. For comparing and mining the resulting multi-layered XML annotations of one article, a Prolog-based approach is used. It focusses on the comparison of XML markup that is distributed among different documents. Prolog predicates have been defined for inferring relations between levels of information that are modelled in separate XML documents. We demonstrate how the Prolog tool is applied in our corpus analyses.
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.
Igel is a small XQuery-based web application for examining a collection of document grammars; in particular, for comparing related document grammars to get a better overview of their differences and similarities. In its initial form, Igel reads only DTDs and provides only simple lists of constructs in them (elements, attributes, notations, parameter entities). Our continuing work is aimed at making Igel provide more sophisticated and useful information about document grammars and building the application into a useful tool for the analysis (and the maintenance!) of families of related document grammars
Different Views on Markup
(2010)
In this chapter, two different ways of grouping information represented in document markup are examined: annotation levels, referring to conceptual levels of description, and annotation layers, referring to the technical realisation of markup using e.g. document grammars. In many current XML annotation projects, multiple levels are integrated into one layer, often leading to the problem of having to deal with overlapping hierarchies. As a solution, we propose a framework for XML-based multiple, independent XML annotation layers for one text, based on an abstract representation of XML documents with logical predicates. Two realisations of the abstract representation are presented, a Prolog fact base format together with an application architecture, and a specification for XML native databases. We conclude with a discussion of projects that have currently adopted this framework.
We describe a general two-stage procedure for re-using a custom corpus for spoken language system development involving a transformation from character-based markup to XML, and DSSSL stylesheet-driven XML markup enhancement with multiple lexical tag trees. The procedure was used to generate a fully tagged corpus; alternatively with greater economy of computing resources, it can be employed as a parametrised ‘tagging on demand’ filter. The implementation will shortly be released as a public resource together with the corpus (German spoken dialogue, about 500k word form tokens) and lexicon (about 75k word form types).