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Funktionsverbgefüge stehen seit jeher in der Sprachkritik, die sich nun auch auf digitale Räume ausbreitet. Vertreten wird dort die These, Funktionsverbgefüge und ihre entsprechenden Basisverben seien äquivalent und könnten in allen Kontexten durch die verbalen Entsprechungen ersetzt werden. Dies kann durch die vorliegende korpusbasierte und textlinguistische Studie am Beispiel des Gefüges Frage stellen widerlegt werden. Anhand eines extensiven Datenmaterials aus den Wikipedia-Artikel-Korpora des IDS zeige ich die semantischen, grammatischen und textlinguistischen Unterschiede zwischen dem Basisverb und dem Funktionsverbgefüge im Gebrauch auf, die sich in der Anreicherung, Verdichtung, Perspektivierung, Gewichtung und Wiederaufnahme von Informationen im Text manifestieren.
This paper describes general requirements for evaluating and documenting NLP tools with a focus on morphological analysers and the design of a Gold Standard. It is argued that any evaluation must be measurable and documentation thereof must be made accessible for any user of the tool. The documentation must be of a kind that it enables the user to compare different tools offering the same service, hence the descriptions must contain measurable values. A Gold Standard presents a vital part of any measurable evaluation process, therefore, the corpus-based design of a Gold Standard, its creation and problems that occur are reported upon here. Our project concentrates on SMOR, a morphological analyser for German that is to be offered as a web-service. We not only utilize this analyser for designing the Gold Standard, but also evaluate the tool itself at the same time. Note that the project is ongoing, therefore, we cannot present final results.
Corpus-based identification and disambiguation of reading indicators for German nominalizations
(2010)
Corpus data is often structurally and lexically ambiguous; corpus extraction methodologies thus must be made aware of ambiguities. Therefore, given an extraction task, all relevant ambiguities must be identified. To resolve these ambiguities, contextual data responsible for one or another reading is to be considered. In the context of our present work, German -ung-nominalizations and their sortal readings are under examination. A number of these nominalizations may be read as an event or a result, depending on the semantic group they belong to. Here, we concentrate on nominalizations of verbs of saying (henceforth: "verba dicendi"), identify their context partners and their influence on the sortal reading of the nominalizations in question. We present a tool which calculates the sortal reading of such nominalizations and thus may improve not only corpus extraction, but also e.g. machine translation. Lastly, we describe successful attempts to identify the correct sortal reading, conclusions and future work.
Das Forschungs- und Lehrkorpus für GesprochenesDeutsch (FOLK) ist ein Korpus des gesprochenen Deutsch in natürlichen sozialen Interaktionen, das seit 2008 in der Abteilung Pragmatik am Leibniz-Institut für Deutsche Sprache in Mannheim aufgebaut wird. FOLK besteht aus Audio- und Videoaufzeichnungen natürlicher Gespräche aus verschiedensten gesellschaftlichen Bereichen (private, institutionelle und öffentliche Interaktionsdomäne), die durch Transkription, weitere Annotationen und Metadaten-Dokumentation für korpusgestützte Analysen erschlossen und zur wissenschaftlichen Nutzung bereitgestellt werden. FOLK wird auf vielfältige Weise für Untersuchungen zum gesprochenen Deutsch genutzt, insbesondere in der Gesprächsforschung, der Korpuslinguistik und anwendungsorientierten Zweigen der Linguistik.
We report on a new project building a Natural Language Processing resource for Zulu by making use of resources already available. Combining tagging results with the results of morphological analysis semi-automatically, we expect to reduce the amount of manual work when generating a finely-grained gold standard corpus usable for training a tagger. From the tagged corpus, we plan to extract verb-argument pairs with the aim of compiling a verb valency lexicon for Zulu.
So far, Sepedi negations have been considered more from the point of view of lexicographical treatment. Theoretical works on Sepedi have been used for this purpose, setting as an objective a neat description of these negations in a (paper) dictionary. This paper is from a different perspective: instead of theoretical works, corpus linguistic methods are used: (1) a Sepedi corpus is examined on the basis of existing descriptions of the occurrences of a relevant verb, looking at its negated forms from a purely prescriptive point of view; (2) a "corpus-driven" strategy is employed, looking only for sequences of negation particles (or morphemes) in order to list occurring constructions, without taking into account the verbs occurring in them, apart from their endings. The approach in (2) is only intended to show a possible methodology to extend existing theories on occurring negations. We would also like to try to help lexicographers to establish a frequency-based order of entries of possible negation forms in their dictionaries by showing them the number of respective occurrences. As with all corpus linguistic work, however, we must regard corpus evidence not as representative, but as tendencies of language use that can be detected and described. This is especially true for Sepedi, for which only few and small corpora exist. This paper also describes the resources and tools used to create the necessary corpus and also how it was annotated with part of speech and lemmas. Exploring the quality of available Sepedi part-of-speech taggers concerning verbs, negation morphemes and subject concords may be a positive side result.
This paper describes the application of probabilistic part of speech taggers to the Dzongkha language. A tag set containing 66 tags is designed, which is based on the Penn Treebank. A training corpus of 40,247 tokens is utilized to train the model. Using the lexicon extracted from the training corpus and lexicon from the available word list, we used two statistical taggers for comparison reasons. The best result achieved was 93.1% accuracy in a 10-fold cross validation on the training set. The winning tagger was thereafter applied to annotate a 570,247 token corpus.
In this article, we examine the current situation of data dissemination and provision for CMC corpora. By that we aim to give a guiding grid for future projects that will improve the transparency and replicability of research results as well as the reusability of the created resources. Based on the FAIR guiding principles for research data management, we evaluate the 20 European CMC corpora listed in the CLARIN CMC Resource family, individuate successful strategies among the existing corpora and establish best practices for future projects. We give an overview of existing approaches to data referencing, dissemination and provision in European CMC corpora, and discuss the methods, formats and strategies used. Furthermore, we discuss the need for community standards and offer recommendations for best practices when creating a new CMC corpus.
Learning from students. On the design and usability of an e-dictionary of mathematical graph theory
(2022)
We created a prototype of an electronic dictionary for the mathematical domain of graph theory. We evaluate our prototype and compare its effectiveness in task-based tests with that of Wikipedia. Our dictionary is based on a corpus; the terms and their definitions were automatically extracted and annotated by experts (cf. Kruse/Heid 2020). The dictionary is bilingual, covering German and English; it gives equivalents, definitions and semantically related terms. For the implementation of the dictionary, we used LexO (Bellandi et al. 2017). The target group of the dictionary are students of mathematics who attend lectures in German and work with English resources. We carried out tests to understand which items the students search for when they work on graph-theoretical tasks. We ran the same test twice, with comparable student groups, either allowing Wikipedia as an information source or our dictionary. The dictionary seems to be especially helpful for students who already have a vague idea of a term because they can use the resource to check if their idea is right.