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Contents:
1. Vasile Pais, Maria Mitrofan, Verginica Barbu Mititelu, Elena Irimia, Roxana Micu and Carol Luca Gasan: Challenges in Creating a Representative Corpus of Romanian Micro-Blogging Text. Pp. 1-7
2. Modest von Korff: Exhaustive Indexing of PubMed Records with Medical Subject Headings. Pp. 8-15
3. Luca Brigada Villa: UDeasy: a Tool for Querying Treebanks in CoNLL-U Format. Pp. 16-19
4. Nils Diewald: Matrix and Double-Array Representations for Efficient Finite State Tokenization. Pp. 20-26
5. Peter Fankhauser and Marc Kupietz: Count-Based and Predictive Language Models for Exploring DeReKo. Pp. 27-31
6. Hanno Biber: “The word expired when that world awoke.” New Challenges for Research with Large Text Corpora and Corpus-Based Discourse Studies in Totalitarian Times. Pp. 32-35
FAIR-Prinzipien und Qualitätskriterien für Transkriptionsdaten. Empfehlungen und offene Fragen
(2022)
Dieser Beitrag behandelt die mittlerweile als Bestandteil guter wissenschaftlicher Praxis anerkannten FAIR-Prinzipien in Bezug auf die Transkription und Annotation gesprochener Sprache und multimodaler Interaktion. Forschungsdaten - und somit Transkriptionsdaten - sollen heute Findable, Accessible, Interoperable und Reusable sein. Der Beitrag versucht dementsprechend, empirische Methoden im Prozess der Digitalisierung und generische Prinzipien des digitalen Forschungsdatenmanagements zusammenzubringen, um für diesen Kontext einem operationalisierten Begriff der „FAIRness“ näher zu kommen und möglichst konkrete Empfehlungen aufzustellen. Der Beitrag sollte aber gleichzeitig zur Diskussion anregen, denn konkrete Anforderungen in Bezug auf das Forschungsdatenmanagement und die Datenqualität müssen auch im Rahmen der FAIR-Prinzipien von den Fachgemeinschaften selbst herausgearbeitet werden.
This paper presents the QUEST project and describes concepts and tools that are being developed within its framework. The goal of the project is to establish quality criteria and curation criteria for annotated audiovisual language data. Building on existing resources developed by the participating institutions earlier, QUEST also develops tools that could be used to facilitate and verify adherence to these criteria. An important focus of the project is making these tools accessible for researchers without substantial technical background and helping them produce high-quality data. The main tools we intend to provide are a questionnaire and automatic quality assurance for depositors of language resources, both developed as web applications. They are accompanied by a knowledge base, which will contain recommendations and descriptions of best practices established in the course of the project. Conceptually, we consider three main data maturity levels in order to decide on a suitable level of strictness of the quality assurance. This division has been introduced to avoid that a set of ideal quality criteria prevent researchers from depositing or even assessing their (legacy) data. The tools described in the paper are work in progress and are expected to be released by the end of the QUEST project in 2022.
Towards comprehensive definitions of data quality for audiovisual annotated language resources
(2021)
Though digital infrastructures such as CLARIN have been successfully established and now provide large collections of digital resources, the lack of widely accepted standards for data quality and documentation still makes re-use of research data a difficult endeavour, especially for more complex resource types. The article gives a detailed overview over relevant characteristics of audiovisual annotated language resources and reviews possible approaches to data quality in terms of their suitability for the current context. Conclusively, various strategies are suggested in order to arrive at comprehensive and adequate definitions of data quality for this specific resource type and possibly for digital language resources in general.
In this paper, we present our experiences and decisions in dealing with challenges in developing, maintaining and operating online research software tools in the field of linguistics. In particular, we highlight reproducibility, dependability, and security as important aspects of quality management – taking into account the special circumstances in which research software
is usually created.
Towards Comprehensive Definitions of Data Quality for Audiovisual Annotated Language Resources
(2020)
Though digital infrastructures such as CLARIN have been successfully established and now provide large collections of digital resources, the lack of widely accepted standards for data quality and documentation still makes re-use of research data a difficult endeavour, especially for more complex resource types. The article gives a detailed overview over relevant characteristics of audiovisual annotated language resources and reviews possible approaches to data quality in terms of their suitability for the current context. Conclusively, various strategies are suggested in order to arrive at comprehensive and adequate definitions of data quality for this particular resource type.
This paper presents the QUEST project and describes concepts and tools that are being developed within its framework. The goal of the project is to establish quality criteria and curation criteria for annotated audiovisual language data. Building on existing resources developed by the participating institutions earlier, QUEST develops tools that could be used to facilitate and verify adherence to these criteria. An important focus of the project is making these tools accessible for researchers without substantial technical background and helping them produce high-quality data. The main tools we intend to provide are the depositors’ questionnaire and automatic quality assurance, both developed as web applications. They are accompanied by a Knowledge base, which will contain recommendations and descriptions of best practices established in the course of the project. Conceptually, we split linguistic data into three resource classes (data deposits, collections and corpora). The class of a resource defines the strictness of the quality assurance it should undergo. This division is introduced so that too strict quality criteria do not prevent researchers from depositing their data.