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Contents:
1. Christoph Kuras, Thomas Eckart, Uwe Quasthoff and Dirk Goldhahn: Automation, management and improvement of text corpus production, S. 1
2. Thomas Krause, Ulf Leser, Anke Lüdeling and Stephan Druskat: Designing a re-usable and embeddable corpus search library, S. 6
3. Radoslav Rábara, Pavel Rychlý and Ondřej Herman: Distributed corpus search, S. 10
4. Adrien Barbaresi and Antonio Ruiz Tinoco: Using elasticsearch for linguistic analysis of tweets in time and space, S. 14
5. Marc Kupietz, Nils Diewald and Peter Fankhauser: How to Get the Computation Near the Data: Improving data accessibility to, and reusability of analysis functions in corpus query platforms, S. 20
6. Roman Schneider: Example-based querying for specialist corpora, S. 26
7. Paul Rayson: Increasing interoperability for embedding corpus annotation pipelines in Wmatrix and other corpus retrieval tools, S. 33
The present submission reports on a pilot project conducted at the Institute for the German Language (IDS), aiming at strengthening the connection between ISO TC37SC4 “Language Resource Management” and the CLARIN infrastructure. In terminology management, attempts have recently been made to use graph-theoretical analyses to get a better understanding of the structure of terminology resources. The project described here aims at applying some of these methods to potentially incomplete concept fields produced over years by numerous researchers serving as experts and editors of ISO standards. The main results of the project are twofold. On the one hand, they comprise concept networks dynamically generated from a relational database and browsable by the user. On the other, the project has yielded significant qualitative feedback that will be offered to ISO. We provide the institutional context of this endeavour, its theoretical background, and an overview of data preparation and tools used. Finally, we discuss the results and illustrate some of them.
In mid-2017, as part of our activities within the TEI Special Interest Group for Linguists (LingSIG), we submitted to the TEI Technical Council a proposal for a new attribute class that would gather attributes facilitating simple token-level linguistic annotation. With this proposal, we addressed community feedback complaining about the lack of a specific tagset for lightweight linguistic annotation within the TEI. Apart from @lemma and @lemmaRef, up till now TEI encoders could only resort to using the generic attribute @ana for inline linguistic annotation, or to the quite complex system of feature structures for robust linguistic annotation, the latter requiring relatively complex processing even for the most basic types of linguistic features. As a result, there now exists a small set of basic descriptive devices which have been made available at the cost of only very small changes to the TEI tagset. The merit of a predefined TEI tagset for lightweight linguistic annotation is the homogeneity of tagging and thus better interoperability of simple linguistic resources encoded in the TEI. The present paper introduces the new attributes, makes a case for one more addition, and presents the advantages of the new system over the legacy TEI solutions.