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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.
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.
In the project SemDok (Generic document structures in linearly organised texts) funded by the German Research Foundation DFG, a discourse parser for a complex type (scientific articles by example), is being developed. Discourse parsing (henceforth DP) according to the Rhetorical Structure Theory (RST) (Mann and Taboada, 2005; Marcu, 2000) deals with automatically assigning a text a tree structure in which discourse segments and rhetorical relations between them are marked, such as Concession. For identifying the combinable segments, declarative rules are employed, which describe linguistic and structural cues and constraints about possible combinations by referring to different XML annotation layers of the input text, and external knowledge bases such as a discourse marker lexicon, a lexico-semantic ontology (later to be combined with a domain ontology), and an ontology of rhetorical relations. In our text-technological environment, the obvious choice of formalism to represent such ontologies is OWL (Smith et al., 2004). In this paper, we describe two OWL ontologies and how they are consulted from the discourse parser to solve certain tasks within DP. The first ontology is a taxononomy of rhetorical relations which was developed in the project. The second one is an OWL version of GermaNet, the model of which we designed together with our project partners.
Knowledge in textual form is always presented as visually and hierarchically structured units of text, which is particularly true in the case of academic texts. One research hypothesis of the ongoing project Knowledge ordering in texts - text structure and structure visualisations as sources of natural ontologies1 is that the textual structure of academic texts effectively mirrors essential parts of the knowledge structure that is built up in the text. The structuring of a modern dissertation thesis (e.g. in the form of an automatically generated table of contents - toes), for example, represents a compromise between requirements of the text type and the methodological and conceptual structure of its subject-matter. The aim of the project is to examine how visual-hierarchical structuring systems are constructed, how knowledge structures are encoded in them, and how they can be exploited to automatically derive ontological knowledge for navigation, archiving, or search tasks. The idea to extract domain concepts and semantic relations mainly from the structural and linguistic information gathered from tables of contents represents a novel approach to ontology learning.
This study examines what kind of cues and constraints for discourse interpretation can be derived from the logical and generic document structure of complex texts by the example of scientific journal articles. We performed statistical analysis on a corpus of scientific articles annotated on different annotations layers within the framework of XML-based multi-layer annotation. We introduce different discourse segment types that constrain the textual domains in which to identify rhetorical relation spans, and we show how a canonical sequence of text type structure categories is derived from the corpus annotations. Finally, we demonstrate how and which text type structure categories assigned to complex discourse segments of the type “block” statistically constrain the occurrence of rhetorical relation types.
A text parsing component designed to be part of a system that assists students in academic reading an writing is presented. The parser can automatically add a relational discourse structure annotation to a scientific article that a user wants to explore. The discourse structure employed is defined in an XML format and is based the Rhetorical Structure Theory. The architecture of the parser comprises pre-processing components which provide an input text with XML annotations on different linguistic and structural layers. In the first version these are syntactic tagging, lexical discourse marker tagging, logical document structure, and segmentation into elementary discourse segments. The algorithm is based on the shift-reduce parser by Marcu (2000) and is controlled by reduce operations that are constrained by linguistic conditions derived from an XML-encoded discourse marker lexicon. The constraints are formulated over multiple annotation layers of the same text.
Der Beitrag untersucht vorhandene Lösungen und neue Möglichkeiten des Korpusausbaus aus Social Media- und internetbasierter Kommunikation (IBK) für das Deutsche Referenzkorpus (DEREKO). DEREKO ist eine Sammlung gegenwartssprachlicher Schriftkorpora am IDS, die der sprachwissenschaftlichen Öffentlichkeit über die Korpusschnittstellen COSMAS II und KorAP angeboten wird. Anhand von Definitionen und Beispielen gehen wir zunächst auf die Extensionen und Überlappungen der Konzepte Social Media, Internetbasierte Kommunikation und Computer-mediated Communication ein. Wir betrachten die rechtlichen Voraussetzungen für einen Korpusausbau aus Sozialen Medien, die sich aus dem kürzlich in relevanten Punkten reformierten deutschen Urheberrecht, aus Persönlichkeitsrechten wie der europäischen Datenschutz-Grundverordnung ergeben und stellen Konsequenzen sowie mögliche und tatsächliche Umsetzungen dar. Der Aufbau von Social Media-Korpora in großen Textmengen unterliegt außerdem korpustechnologischen Herausforderungen, die für traditionelle Schriftkorpora als gelöst galten oder gar nicht erst bestanden. Wir berichten, wie Fragen der Datenaufbereitung, des Korpus-Encoding, der Anonymisierung oder der linguistischen Annotation von Social Media Korpora für DEREKO angegangen wurden und welche Herausforderungen noch bestehen. Wir betrachten die Korpuslandschaft verfügbarer deutschsprachiger IBK- und Social Media-Korpora und geben einen Überblick über den Bestand an IBK- und Social Media-Korpora und ihre Charakteristika (Chat-, Wiki Talk- und Forenkorpora) in DEREKO sowie von laufenden Projekten in diesem Bereich. Anhand korpuslinguistischer Mikro- und Makro-Analysen von Wikipedia-Diskussionen im Vergleich mit dem Gesamtbestand von DEREKO zeigen wir charakterisierende sprachliche Eigenschaften von Wikipedia-Diskussionen auf und bewerten ihren Status als Repräsentant von IBK-Korpora.
Im Teilprojekt CI “SemDok” der DFG-Forschergruppe Texttechnologische Informationsmodellierung wurde ein Textparser für Diskursstrukturen wissenschaftlicher Zeitschriftenartikel nach der Rhetorical Structure Theory entwickelt. Die wesentlichen konzeptuellen und technischen Merkmale des Chart-Parsers und die sich daraus ergebenden Parametrisierungsmöglichkeiten für Parsing-Experimente werden beschrieben. Zudem wird HPVtz., ein Tool für die Visualisierung von Parsing-Ergebnissen (RST-Bäume in einer XML-Anwendung) und die Navigation in ihnen, vorgestellt.
Linguistische Annotationen für die Analyse von Gliederungsstrukturen wissenschaftlicher Texte
(2012)
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.
Editorial
(2011)
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 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.
Für die spezifischen Bedürfnisse der Schreibbeobachtung wurde das Orthografische Kernkorpus (OKK) als virtuelles Korpus in DeReKo entwickelt. Mit derzeit rund 14 Mrd. Token deckt es den Schriftsprachgebrauch in den deutschsprachigen Ländern im Zeitraum von 1995 bis in die Gegenwart ab. Der Zugriff über die Korpusanalyseplattform KorAP erlaubt nicht nur die Nutzung verschiedener Annotationen, sondern über die API-Schnittstellen auch die Einbindung in diverse Auswertungsumgebungen wie RStudio über den RKorAPClient und macht es so für zahlreiche Analyse- und Visualisierungsmöglichkeiten zugänglich.