Korpuslinguistik
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This paper presents an extended annotation and analysis of interpretative reply relations focusing on a comparison of reply relation types and targets between conflictual pages and neutral pages of German Wikipedia (WP) talk pages. We briefly present the different categories identified for interpretative reply relations to analyze the relationship between WP postings as well as linguistic cues for each category. We investigate referencing strategies of WP authors in discussion page postings, illustrated by means of reply relation types and targets taking into account the degree of disagreement displayed on a WP talk page. We provide richly annotated data that can be used for further analyses such as the identification of interactional relations on higher levels, or for training tasks in machine learning algorithms.
This paper presents the IVK-Ler corpus, a longitudinal, annotated learner corpus of weekly writings produced by a group of 18 adolescents in a preparatory class. The corpus consists of 117 student texts collected between 2020 and 2021 and has a structure layered by student and text number. It includes metadata that enables researchers to analyze and track individual student progress in terms of syntactic competence and literacy. The annotation schema, manual and automatic annotation processes, and corpus representation are described in detail. The corpus currently includes target hypotheses and gold standard part-of-speech tags. Future work could include additional annotation layers for topological fields and dependency relations, as well as semantic and discourse annotations to make the corpus usable for tasks beyond syntactic evaluations.
Der Beitrag illustriert die Nutzung des Forschungs- und Lehrkorpus Gesprochenes Deutsch (FOLK) für interaktionslinguistische Fragestellungen anhand einer exemplarischen Studie. Zunächst werden die Stratifikation (Datenkomposition) des Korpus, das zugrundeliegende Datenmodell und dessen Annotationsebenen sowie Typen von Untersuchungsinteressen vorgestellt, für die das Korpus nutzbar ist. Im Hauptteil wird Schritt für Schritt anhand einer Studie zur Verwendung des Formats was heißt X in der sozialen Interaktion gezeigt, wie mit FOLK relevante Daten gefunden und analysiert werden können. Abschließend weisen wir auf einige Vorsichtsmaßnahmen bei der Benutzung des Korpus hin.
The QUEST (QUality ESTablished) project aims at ensuring the reusability of audio-visual datasets (Wamprechtshammer et al., 2022) by devising quality criteria and curating processes. RefCo (Reference Corpora) is an initiative within QUEST in collaboration with DoReCo (Documentation Reference Corpus, Paschen et al. (2020)) focusing on language documentation projects. Previously, Aznar and Seifart (2020) introduced a set of quality criteria dedicated to documenting fieldwork corpora. Based on these criteria, we establish a semi-automatic review process for existing and work-in-progress corpora, in particular for language documentation. The goal is to improve the quality of a corpus by increasing its reusability. A central part of this process is a template for machine-readable corpus documentation and automatic data verification based on this documentation. In addition to the documentation and automatic verification, the process involves a human review and potentially results in a RefCo certification of the corpus. For each of these steps, we provide guidelines and manuals. We describe the evaluation process in detail, highlight the current limits for automatic evaluation and how the manual review is organized accordingly.
Metadata provides important information relevant both to finding and understanding corpus data. Meaningful linguistic data requires both reasonable annotations and documentation of these annotations. This documentation is part of the metadata of a dataset. While corpus documentation has often been provided in the form of accompanying publications, machinereadable metadata, both containing the bibliographic information and documenting the corpus data, has many advantages. Metadata standards allow for the development of common tools and interfaces. In this paper I want to add a new perspective from an archive’s point of view and look at the metadata provided for four learner corpora and discuss the suitability of established standards for machine-readable metadata. I am are aware that there is ongoing work towards metadata standards for learner corpora. However, I would like to keep the discussion going and add another point of view: increasing findability and reusability of learner corpora in an archiving context.
This paper describes the TEI-based ISO standard 24624:2016 ‘Transcription of spoken language’ and other formats used within CLARIN for spoken language resources. It assesses the current state of support for the standard and the interoperability between these formats and with rele- vant tools and services. The main idea behind the paper is that a digital infrastructure providing language resources and services to researchers should also allow the combined use of resources and/or services from different contexts. This requires syntactic and semantic interoperability. We propose a solution based on the ISO/TEI format and describe the necessary steps for this format to work as an exchange format with basic semantic interoperability for spoken language resources across the CLARIN infrastructure and beyond.
This article presents a discussion on the main linguistic phenomena which cause difficulties in the analysis of user-generated texts found on the web and in social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework of syntactic analysis. Given on the one hand the increasing number of treebanks featuring user-generated content, and its somewhat inconsistent treatment in these resources on the other, the aim of this article is twofold: (1) to provide a condensed, though comprehensive, overview of such treebanks—based on available literature—along with their main features and a comparative analysis of their annotation criteria, and (2) to propose a set of tentative UD-based annotation guidelines, to promote consistent treatment of the particular phenomena found in these types of texts. The overarching goal of this article is to provide a common framework for researchers interested in developing similar resources in UD, thus promoting cross-linguistic consistency, which is a principle that has always been central to the spirit of UD.
Twenty-two historical encyclopedias encoded in TEI: a new resource for the Digital Humanities
(2020)
This paper accompanies the corpus publication of EncycNet, a novel XML/TEI annotated corpus of 22 historical German encyclopedias from the early 18th to early 20th century. We describe the creation and annotation of the corpus, including the rationale for its development, suggested methodology for TEI annotation, possible use cases and future work. While many well-developed annotation standards for lexical resources exist, none can adequately model the encyclopedias at hand, and we therefore suggest how the TEI Lex-0 standard may be modified with additional guidelines for the annotation of historical encyclopedias. As the digitization and annotation of historical encyclopedias are settling on TEI as the de facto standard, our methodology may inform similar projects.
We present web services which implement a workflow for transcripts of spoken language following the TEI guidelines, in particular ISO 24624:2016 “Language resource management – Transcription of spoken language”. The web services are available at our website and will be available via the CLARIN infrastructure, including the Virtual Language Observatory and WebLicht.
Corpus REDEWIEDERGABE
(2020)
This article presents the corpus REDEWIEDERGABE, a German-language historical corpus with detailed annotations for speech, thought and writing representation (ST&WR). With approximately 490,000 tokens, it is the largest resource of its kind. It can be used to answer literary and linguistic research questions and serve as training material for machine learning. This paper describes the composition of the corpus and the annotation structure, discusses some methodological decisions and gives basic statistics about the forms of ST&WR found in this corpus.