Korpuslinguistik
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In the first volume of Corpus Linguistics and Linguistic Theory, Gries (2005. Null-hypothesis significance testing of word frequencies: A follow-up on Kilgarriff. Corpus Linguistics and Linguistic Theory 1(2). doi:10.1515/cllt.2005.1.2.277. http://www.degruyter.com/view//cllt.2005.1.issue-2/cllt.2005.1.2.277/cllt.2005.1.2.277.xml: 285) asked whether corpus linguists should abandon null-hypothesis significance testing. In this paper, I want to revive this discussion by defending the argument that the assumptions that allow inferences about a given population – in this case about the studied languages – based on results observed in a sample – in this case a collection of naturally occurring language data – are not fulfilled. As a consequence, corpus linguists should indeed abandon null-hypothesis significance testing.
Distributional models of word use constitute an indispensable tool in corpus based lexicological research for discovering paradigmatic relations and syntagmatic patterns (Belica et al. 2010). Recently, word embeddings (Mikolov et al. 2013) have revived the field by allowing to construct and analyze distributional models on very large corpora. This is accomplished by reducing the very high dimensionality of word cooccurrence contexts, the size of the vocabulary, to few dimensions, such as 100-200. However, word use and meaning can vary widely along dimensions such as domain, register, and time, and word embeddings tend to represent only the most prevalent meaning. In this paper we thus construct domain specific word embeddings to allow for systematically analyzing variations in word use. Moreover, we also demonstrate how to reconstruct domain specific co-occurrence contexts from the dense word embeddings.
Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.
Since 2013 representatives of several French and German CMC corpus projects have developed three customizations of the TEI-P5 standard for text encoding in order to adapt the encoding schema and models provided by the TEI to the structural peculiarities of CMC discourse. Based on the three schema versions, a 4th version has been created which takes into account the experiences from encoding our corpora and which is specifically designed for the submission of a feature request to the TEI council. On our poster we would present the structure of this schema and its relations (commonalities and differences) to the previous schemas.
Das Archiv für Gesprochenes Deutsch (AGD, Stift/Schmidt 2014) am Leibniz-Institut für Deutsche Sprache ist ein Forschungsdatenzentrum für Korpora des gesprochenen Deutsch. Gegründet als Deutsches Spracharchiv (DSAv) im Jahre 1932 hat es über Eigenprojekte, Kooperationen und Übernahmen von Daten aus abgeschlossenen Forschungsprojekten einen Bestand von bald 100 Variations-, Interview- und Gesprächskorpora aufgebaut, die u. a. dialektalen Sprachgebrauch, mündliche Kommunikationsformen oder die Sprachverwendung bestimmter Sprechertypen oder zu bestimmten Themen dokumentieren. Heute ist dieser Bestand fast vollständig digitalisiert und wird zu einem großen Teil der wissenschaftlichen Gemeinschaft über die Datenbank für Gesprochenes Deutsch (DGD) im Internet zur Nutzung in Forschung und Lehre angeboten.
The present paper examines a variety of ways in which the Corpus of Contemporary Romanian Language (CoRoLa) can be used. A multitude of examples intends to highlight a wide range of interrogation possibilities that CoRoLa opens for different types of users. The querying of CoRoLa displayed here is supported by the KorAP frontend, through the querying language Poliqarp. Interrogations address annotation layers, such as the lexical, morphological and, in the near future, the syntactical layer, as well as the metadata. Other issues discussed are how to build a virtual corpus, how to deal with errors, how to find expressions and how to identify expressions.
Introduction
(2019)
Die korpusbasierte Lexikografie ist ein interessanter und vielfältiger wissenschaftlicher Anwendungsbereich, der auch im muttersprachlichen Deutschunterricht und im Deutsch-als-Fremdsprache-Unterricht eine größere Rolle einnehmen sollte. In unserem Beitrag stellen wir deshalb geeignete Korpora und Korpusanalysewerkzeuge vor, mit deren Hilfe Nutzerinnen und Nutzer einzelne Angabebereiche in einem Wörterbuch nicht nur nachvollziehen, sondern auch eigenständig erarbeiten können. Neben vorhandenen Ansätzen geschieht dies am Beispiel des Denktionarys, eines wikibasierten Wörterbuches, für das Schülerinnen und Schüler im Rahmen des Projekts Schüler machen Wörterbücher – Wörterbücher machen Schule im muttersprachlichen Deutschunterricht selbst korpusbasierte Artikel verfassten.
Little strokes fell great oaks. Creating CoRoLa, the reference corpus of contemporary Romanian
(2019)
The paper presents the quite long-standing tradition of Romanian corpus acquisition and processing, which reaches its peak with the reference corpus of contemporary Romanian language (CoRoLa). The paper describes decisions behind the kinds of texts collected, as well as processing and annotation steps, highlighting the structure and importance of metadata to the corpus. The reader is also introduced to the three ways in which (s)he can plunge into the rich linguistic data of the corpus, waiting to be discovered. Besides querying the corpus, word embeddings extracted from it are useful to various natural language processing applications and for linguists, when user-friendly interfaces offer them the possibility to exploit the data.
Digitale Korpora haben die Voraussetzungen, unter denen sich Wissenschaftler mit der Erforschung von Sprachphänomenen beschäftigen, fundamental verändert. Umfangreiche Sammlungen geschriebener und gesprochener Sprache bilden mittlerweile die empirische Basis für mathematisch präzise Generalisierungen über zu beschreibende Wirklichkeitsausschnitte. Das Datenmaterial ist hochkomplex und besteht neben den Rohtexten aus diversen linguistischen Annotationsebenen sowie außersprachlichen Metadaten. Als unmittelbare Folge stellt sich die Konzeption adäquater Recherchelösungen als beträchtliche Herausforderung dar. Im vorliegenden Buch wird deshalb ein datenbankbasierter Ansatz vorgestellt, der sich der Problematiken multidimensionaler Korpusrecherchen annimmt. Ausgehend von einer Charakterisierung der Anforderungsmerkmale linguistisch motivierter Suchen werden Speicherungs- und Abfragestrategien für mehrfach annotierte Korpora entwickelt und anhand eines linguistischen Anforderungskatalogs evaluiert. Ein Schwerpunkt liegt dabei in der Einführung problemorientierter Segmentierung und Parallelisierung.
Text corpora come in many different shapes and sizes and carry heterogeneous annotations, depending on their purpose and design. The true benefit of corpora is rooted in their annotation and the method by which this data is encoded is an important factor in their interoperability. We have accumulated a large collection of multilingual and parallel corpora and encoded it in a unified format which is compatible with a broad range of NLP tools and corpus linguistic applications. In this paper, we present our corpus collection and describe a data model and the extensions to the popular CoNLL-U format that enable us to encode it.
Neues von KorAP
(2019)
Persuasionsstrategien in deutschen rechtsorientierten Zeitungen. Eine korpuslinguistische Studie
(2019)
Corpus Linguistics has often proved fruitful to examine different types of discourses, also the one of refugees. Aim of the paper is to show how language usage patterns can be focused on with the help of techniques grounded in Corpus Linguistics, giving information about themes and topoi. After showing what type of words (keywords, collocations) and what type of phenomena will be considered (topoi, metaphors and frames) in the article, the focus will shift on the methodology and the adopted criteria. After presenting the primary corpus (articles from right-oriented newspapers) and the comparison corpus (articles from 'Die Zeit') the main results of the analysis are presented and reflected on.
Contents:
1. Johannes Graën, Tannon Kew, Anastassia Shaitarova and Martin Volk, "Modelling Large Parallel Corpora", S. 1-8
2. Pedro Javier Ortiz Suárez, Benoît Sagot and Laurent Romary, "Asynchronous Pipelines for Processing Huge Corpora on Medium to Low Resource Infrastructures", S. 9-16
3. Vladimír Benko, "Deduplication in Large Web Corpora", S. 17-22
4. Mark Davies, "The best of both worlds: Multi-billion word “dynamic” corpora", S. 23-28
5. Adrien Barbaresi, "On the need for domain-focused web corpora", S. 29-32
6. Marc Kupietz, Eliza Margaretha, Nils Diewald, Harald Lüngen and Peter Fankhauser, "What's New in EuReCo? Interoperability, Comparable Corpora, Licensing", S. 33-39
This contribution presents a quantitative approach to speech, thought and writing representation (ST&WR) and steps towards its automatic detection. Automatic detection is necessary for studying ST&WR in a large number of texts and thus identifying developments in form and usage over time and in different types of texts. The contribution summarizes results of a pilot study: First, it describes the manual annotation of a corpus of short narrative texts in relation to linguistic descriptions of ST&WR. Then, two different techniques of automatic detection – a rule-based and a machine learning approach – are described and compared. Evaluation of the results shows success with automatic detection, especially for direct and indirect ST&WR.