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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 paper describes the TEI-based ISO standard 2462:2016 “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 relevant 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.
Signposts for CLARIN
(2021)
An implementation of CMDI-based signposts and its use is presented in this paper. Arnold, Fisseni et al. (2020) present signposts as a solution to challenges in long-term preservation of corpora. Though applicable to digital resources in general, we focus on corpora, especially those that are continuously extended or subject to modification, e.g., due to legal injunctions, but also may overlap with respect to constituents, and may be subject to migrations to new data formats. We describe the contribution signposts can make to the CLARIN infrastructure, notably virtual collections, and document the design for the CMDI profile.
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.
Signposts for CLARIN
(2020)
An implementation of CMDI-based signposts and its use is presented in this paper. Arnold et al. 2020 present Signposts as a solution to challenges in long-term preservation of corpora, especially corpora that are continuously extended and subject to modification, e.g., due to legal injunctions, but also may overlap with respect to constituents, and may be subject to migrations to new data formats. We describe the contribution Signposts can make to the CLARIN infrastructure and document the design for the CMDI profile.
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.
Unlike traditional text corpora collected from trustworthy sources, the content of web based corpora has to be filtered. This study briefly discusses the impact of web spam on corpus usability and emphasizes the importance of removing computer generated text from web corpora.
The paper also presents a keyword comparison of an unfiltered corpus with the same collection of texts cleaned by a supervised classifier trained using FastText. The classifier was able to recognize 71% of web spam documents similar to the training set but lacked both precision and recall when applied to short texts from another data set.
The Manatee corpus management system on which the Sketch Engine is built is efficient, but unable to harness the power of today’s multiprocessor machines. We describe a new, compatible implementation of Manatee which we develop in the Go language and report on the performance gains that we obtained.