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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.
The actual or anticipated impact of research projects can be documented in scientific publications and project reports. While project reports are available at varying level of accessibility, they might be rarely used or shared outside of academia. Moreover, a connection between outcomes of actual research project and potential secondary use might not be explicated in a project report. This paper outlines two methods for classifying and extracting the impact of publicly funded research projects. The first method is concerned with identifying impact categories and assigning these categories to research projects and their reports by extension by using subject matter experts; not considering the content of research reports. This process resulted in a classification schema that we describe in this paper. With the second method which is still work in progress, impact categories are extracted from the actual text data.
How can we measure the impact – such as awareness for economic, ecological, and political matters – of information, such as scientific publications, user-generated content, and reports from the public administration, based on text data? This workshop brings together research from different theoretical paradigms and methodologies for the extraction of impact-relevant indicators from natural language text data and related meta-data. The papers in this workshop represent different types of expertise in different methods for analyzing text data; spanning the whole spectrum of qualitative, quantitative, and mixed methods techniques, as well as domain expertise in the field of impact measurement. The program was built to create an interdisciplinary half-day workshop where we discuss possibilities, limitations, and synergistic effects of different approaches.
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.
Negation is an important contextual phenomenon that needs to be addressed in sentiment analysis. Next to common negation function words, such as not or none, there is also a considerably large class of negation content words, also referred to as shifters, such as the verbs diminish, reduce or reverse. However, many of these shifters are ambiguous. For instance, spoil as in spoil your chance reverses the polarity of the positive polar expression chance while in spoil your loved ones, no negation takes place. We present a supervised learning approach to disambiguating verbal shifters. Our approach takes into consideration various features, particularly generalization features.
Complement phrases are essential for constructing well-formed sentences in German. Identifying verb complements and categorizing complement classes is challenging even for linguists who are specialized in the field of verb valency. Against this background, we introduce an ML-based algorithm which is able to identify and classify complement phrases of any German verb in any written sentence context. We use a large training set consisting of example sentences from a valency dictionary, enriched with POS tagging, and the ML-based technique of Conditional Random Fields (CRF) to generate the classification models.
This paper discusses current trends in DeReKo, the German Reference Corpus, concerning legal issues around the recent German copyright reform with positive implications for corpus building and corpus linguistics in general, recent corpus extensions in the genres of popular magazines, journals, historical texts, and web-based football reports. Besides, DeReKo is finally accessible via the new
corpus research platform KorAP, offering registered users several news features in comparison with its predecessor COSMAS II.
German is a language with complex morphological processes. Its long and often ambiguous word forms present a bottleneck problem in natural language processing. As a step towards morphological analyses of high quality, this paper introduces a morphological treebank for German. It is derived from the linguistic database CELEX which is a standard resource for German morphology. We build on its refurbished, modernized and partially revised version. The derivation of the morphological trees is not trivial, especially for such cases of conversions which are morpho-semantically opaque and merely of diachronic interest. We develop solutions and present exemplary analyses. The resulting database comprises about 40,000 morphological trees of a German base vocabulary whose format and grade of detail can be chosen according to the requirements of the applications. The Perl scripts for the generation of the treebank are publicly available on github. In our discussion, we show some future directions for morphological treebanks. In particular, we aim at the combination with other reliable lexical resources such as GermaNet.