Open Access

In research data management, descriptive metadata are indispensable to describing data and are a key element in preparing data according to the FAIR principles (Wilkinson et al., 2016). Extracting semantic metadata from textual research data is currently not part of most metadata workflows, even more so if a research data set can be subdivided into smaller parts, such as a newspaper corpus containing multiple newspaper articles. Our approach is to add semantic metadata at the text level to facilitate the search over data. We show how to enrich metadata with three NLP methods: named entity recognition, keyword extraction, and topic modeling. The goal is to make it possible to search for texts that are about certain topics or described by certain keywords, or to identify people, places, and organisations mentioned in texts without actually having to read them and at the same time facilitate the creation of task-tailored subcorpora. To enhance this usability of the data we explore options based on the German Reference Corpus DeReKo, the largest linguistically motivated collection of German language material (Kupietz & Keibel, 2009; Kupietz et al., 2010, 2018), which contains multiple newspapers, books, transcriptions, etc., and enrich its metadata on the level of subportions, i.e. newspaper articles. We received access to a number of data files in DeReKo’s native XML format, I5. To develop the methodology, we focus on a single XML file containing all issues of one newspaper of a whole year. The following sections only give an overview of our approach, we intend, however, to provide a detailed description of the experiments and the selection of data in a subsequent longer contribution.

The International Comparable Corpus (ICC) (Kirk/Čermáková 2017; Čermáková et al. 2021) is an open initiative which aims to improve the empirical basis for contrastive linguistics by compiling comparable corpora for many languages and making them as freely available as possible as well as providing tools with which they can easily be queried and analysed. In this contribution we present the first release of written language parts of the ICC which includes corpora for Chinese, Czech, English, German, Irish (partly), and Norwegian. Each of the released corpora contains 400k words distributed over 14 different text categories according to the ICC specifications. Our poster covers the design basics of the ICC, its TEI encoding, a demonstration of using the ICC via different query tools, and an outlook on future plans. Similar to the European Reference Corpus EuReCo (Kupietz et al. 2020), ICC follows the approach of reusing existing linguistic resources wherever possible in order to cover as many languages as possible with realistic effort in as short a time as possible. In contrast to EuReCo, however, comparable corpus pairs are not defined dynamically in the usage phase, but the compositions of the corpora are fixed in the ICC design. The approaches are thus complementary in this respect. The design principles and composition of the ICC are based on those of the International Corpus of English (ICE) (Greenbaum (ed.) 1996), with the deviation that the ICC includes the additional text category blog post and excludes spoken legal texts (see Čermáková et al. 2021 for details). ICC’s fixed-design approach has the advantage that all single-language corpora in the ICC have the same composition with respect to the selected text types and that this guarantees that the selected broad spectrum of potential influencing variables for linguistic variation is always represented. The disadvantage, however, is that this can only be achieved for quite small corpora and that the generalisability of comparative findings based on the ICC corpora will often need to be checked on larger monolingual corpora or translation corpora (Čermáková/Ebeling/Oksefjell Ebeling forthcoming). Arguing that such issues with comparability and representativeness are inevitable, in one way or the other, and need to be dealt with, our poster will discuss and exemplify the text selections in more detail.