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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.
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.
Researchers in many disciplines, sometimes working in close cooperation, have been concerned with modeling textual data in order to account for texts as the prime information unit of written communication. The list of disciplines includes computer science and linguistics as well as more specialized disciplines like computational linguistics and text technology. What many of these efforts have in common is the aim to model textual data by means of abstract data types or data structures that support at least the semi-automatic processing of texts in any area of written communication.
In this contribution, we discuss and compare alternative options of modelling the entities and relations of wordnet-like resources in the Web Ontology Language OWL. Based on different modelling options, we developed three models of representing wordnets in OWL, i.e. the instance model, the dass model, and the metaclass model. These OWL models mainly differ with respect to the ontological Status of lexical units (word senses) and the synsets. While in the instance model lexical units and synsets are represented as individuals, in the dass model they are represented as classes; both model types can be encoded in the dialect OWL DL. As a third alternative, we developed a metaclass model in OWL FULL, in which lexical units and synsets are defined as metaclasses, the individuals of which are classes themselves. We apply the three OWL models to each of three wordnet-style resources: (1) a subset of the German wordnet GermaNet, (2) the wordnet-style domain ontology TermNet, and (3) GermaTermNet, in which TermNet technical terms and GermaNet synsets are connected by means of a set of “plug-in” relations. We report on the results of several experiments in which we evaluated the performance of querying and processing these different models: (1) A comparison of all three OWL models (dass, instance, and metaclass model) of TermNet in the context of automatic text-to-hypertext conversion, (2) an investigation of the potential of the GermaTermNet resource by the example of a wordnet-based semantic relatedness calculation.
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.