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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.
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.
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
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.
The European digital research infrastructure CLARIN (Common Language Resources and Technology Infrastructure) is building a Knowledge Sharing Infrastructure (KSI) to ensure that existing knowledge and expertise is easily available both for the CLARIN community and for the humanities research communities for which CLARIN is being developed. Within the Knowledge Sharing Infrastructure, so called Knowledge Centres comprise one or more physical institutions with particular expertise in certain areas and are committed to providing their expertise in the form of reliable knowledge-sharing services. In this paper, we present the ninth K Centre – the CLARIN Knowledge Centre for Linguistic Diversity and Language Documentation (CKLD) – and the expertise and services provided by the member institutions at the Universities of London (ELAR/SWLI), Cologne (DCH/IfDH/IfL) and Hamburg (HZSK/INEL). The centre offers information on current best practices, available resources and tools, and gives advice on technological and methodological matters for researchers working within relevant fields.
The sentiment polarity of a phrase does not only depend on the polarities of its words, but also on how these are affected by their context. Negation words (e.g. not, no, never) can change the polarity of a phrase. Similarly, verbs and other content words can also act as polarity shifters (e.g. fail, deny, alleviate). While individually more sparse, they are far more numerous. Among verbs alone, there are more than 1200 shifters. However, sentiment analysis systems barely consider polarity shifters other than negation words. A major reason for this is the scarcity of lexicons and corpora that provide information on them. We introduce a lexicon of verbal polarity shifters that covers the entirety of verbs found in WordNet. We provide a fine-grained annotation of individual word senses, as well as information for each verbal shifter on the syntactic scopes that it can affect.
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.
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.
The paper describes preliminary studies regarding the usage of Example-Based Querying for specialist corpora. We outline an infrastructure for its application within the linguistic domain. Example-Based Querying deals with retrieval situations where users would like to explore large collections of specialist texts semantically, but are unable to explicitly name the linguistic phenomenon they look for. As a way out, the proposed framework allows them to input prototypical everyday language examples or cases of doubt, which are automatically processed by CRF and linked to appropriate linguistic texts in the corpus.