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This paper introduces the recently started DRuKoLA-project that aims at providing mechanisms to flexibly draw virtual comparable corpora from the German Reference Corpus DeReKo and the Reference Corpus of Contemporary Romanian Language CoRoLa in order to use these virtual corpora as empirical basis for contrastive linguistic research.
The present paper describes Corpus Query Lingua Franca (ISO CQLF), a specification designed at ISO Technical Committee 37 Subcommittee 4 “Language resource management” for the purpose of facilitating the comparison of properties of corpus query languages. We overview the motivation for this endeavour and present its aims and its general architecture. CQLF is intended as a multi-part specification; here, we concentrate on the basic metamodel that provides a frame that the other parts fit in.
We present SPLICR, the Web-based Sustainability Platform for Linguistic Corpora and Resources. The system is aimed at people who work in Linguistics or Computational Linguistics: a comprehensive database of metadata records can be explored in order to find language resources that could be appropriate for one’s spe cific research needs. SPLICR also provides a graphical interface that enables users to query and to visualise corpora. The project in which the system is developed aims at sustainably archiving the ca. 60 language resources that have been constructed in three collaborative research centres. Our project has two primary goals: (a) To process and to archive sustainably the resources so that they are still available to the research community in five, ten, or even 20 years time. (b) To enable researchers to query the resources both on the level of their metadata as well as on the level of linguistic annotations. In more general terms, our goal is to enable solutions that leverage the interoperability, reusability, and sustainability of heterogeneous collec- tions of language resources.
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.
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
Integrated Linguistic Annotation Models and Their Application in the Domain of Antecedent Detection
(2011)
Seamless integration of various, often heterogeneous linguistic resources in terms of their output formats and a combined analysis of the respective annotation layers are crucial tasks for linguistic research. After a decade of concentration on the development of formats to structure single annotations for specific linguistic issues, in the last years a variety of specifications to store multiple annotations over the same primary data has been developed. The paper focuses on the integration of the knowledge resource logical document structure information into a text document to enhance the task of automatic anaphora resolution both for the task of candidate detection and antecedent selection. The paper investigates data structures necessary for knowledge integration and retrieval.