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Open Science and language data: Expectations vs. reality. The role of research data infrastructures
(2023)
Language data are essential for any scientific endeavor. However, unlike numerical data, language data are often protected by copyright, as they easily meet the threshold of originality. The role of research infrastructures (such CLARIN, DARIAH, and Text+) is to bridge the gap between uses allowed by statutory exceptions and the requirements of Open Science. This is achieved on the one hand by sharing language data produced by research organisations with the widest possible circle of persons, and on the other by mutualizing efforts towards copyright clearance and appropriate licensing of datasets.
Ziel dieser Arbeit war es, eine Software zu entwickeln, die quantitative und qualitative korpuslinguistische Methoden miteinander verbindet. Die Gesamtarbeit besteht daher aus zwei Teilen: einer Open-Source-Software und dem schriftlichen Teil. Der hier vorgelegte schriftliche Teil ist eine vollständige Dokumentation (Handbuch), ergänzt um eigene Publikationen, die im Rahmen des Dissertationsprojekts entstanden. In Kapitel 1.2 Korpora und beispielhafte Fragestellungen (S. 8) erfolgt eine Illustration beispielhafter Forschungsfragen anhand bereitgestellter und im Corpus- Explorer integrierter Korpora. Außerdem werden unter "?? ?? (S. ??)" Analysen mit verschiedensten prototypischen Forschungsfragen verknüpft, die sowohl quantitative als auch qualitative Perspektiven einnehmen. Der CorpusExplorer wurde besonders nutzerfreundlich gestaltet. Dabei ist die Zielgruppe der Software sehr breit defniert: Die Nutzung soll sowohl in der Forschung als auch in der Lehre möglich sein. Daher richtet sich der CorpusExplorer gleichermaßen an Studierende und Forschende mit ihren jeweils spezifschen Bedürfnissen. Die Nutzung für die Forschung zeigt sich (A) an den integrierten Artikeln sowie daran, dass (B) andere Forschende den CorpusExplorer bereits für ihre Arbeit aufgegriffen haben. Der Nutzen für die Lehre wurde mehrfach selbst erprobt und optimiert. Im Lehr-Einsatz ist es wichtig, dass Korpora mit wenigen Mausklicks analysefertig sind und verschiedene Analysen und Visualisierungen direkt genutzt werden können. Studierende erhalten so die Möglichkeit, eigenes Korpusmaterial direkt und selbst auszuwerten. Für Forschende bietet der CorpusExplorer ein sehr breites Funktionsspektrum. Im Vergleich zu anderer (öffentlich verfügbarer) korpuslinguistischer Software verfügt er aktuell über das wohl breiteste Anwendungsspektrum (51 Analysemodule (inkl. weiterentwickelter Verfahren), über 100 unterstützte Dateiformate für Im- und Export, unterschiedliche Tagger mit 69 unterstützten Sprachmodellen). Er kann so in bestehende Skripte, Toolchains und Workflows für sehr unterschiedliche Forschungsfragen integriert werden. Im CorpusExplorer wurden nicht nur bestehende Funktionen gebündelt, es wurden auch bisherige Verfahren weiterentwickelt. Hierzu zählen z. B. (1) die Entwicklung einer eigenen, an korpuslinguistischen Bedürfnissen ausgerichteten Datenbank- Struktur, (2) die Weiterentwicklung bzw. Optimierung des Verfahrens der Kookkurrenz- Analyse hin zu einer quantitativen Kookkurrenz-Analyse (keine Parameter wie Suchfenstergröße oder Suchwort nötig, Berechnung aller Kookkurrenzen zu allen Token in einem Korpus) und (3) die Verknüpfung unterschiedlicher Analyseressourcen, wie z. B. der NGram- und der Kookkurrenz-Analyse.
Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations
(2009)
In this paper we show that the extraction of opinions from free-text reviews can improve the accuracy of movie recommendations. We present three approaches to extract movie aspects as opinion targets and use them as features for the collaborative filtering. Each of these approaches requires different amounts of manual interaction. We collected a data set of reviews with corresponding ordinal (star) ratings of several thousand movies to evaluate the different features for the collaborative filtering. We employ a state-of-the-art collaborative filtering engine for the recommendations during our evaluation and compare the performance with and without using the features representing user preferences mined from the free-text reviews provided by the users. The opinion mining based features perform significantly better than the baseline, which is based on star ratings and genre information only.
Making research data publicly available for evaluation or reuse is a fundamental part of good scientific practice. However, regulations such as copyright law can prevent this practice and thereby hamper scientific progress. In Germany, text-based research disciplines have for a long time been mostly unable to publish corpora made from material outside of the public domain, effectively excluding contemporary works. While there are approaches to obfuscate text material in a way that it is no longer covered by the original copyright, many use cases still require the raw textual context for evaluation or follow-up research. Recent changes in copyright now permit text and data mining on copyrighted works. However, questions regarding reusability and sharing of such corpora at a later time are still not answered to a satisfying degree. We propose a workflow that allows interested third parties to access customized excerpts of protected corpora in accordance with current German copyright law and the soon to be implemented guidelines of the Digital Single Market directive. Our prototype is a very lightweight web interface that builds on commonly used repository software and web standards.
New exceptions for Text and Data Mining and their possible impact on the CLARIN infrastructure
(2018)
The proposed paper discusses new exceptions for Text and Data Mining that have recently been adopted in some EU Member States, and probably will soon be adopted also at the EU level. These exceptions are of great significance for language scientists, as they exempt those who compile corpora from the obligation to obtain authorisation from rightholders. However, corpora compiled on the basis of such exceptions cannot be freely shared, which in a long run may have serious consequences for Open Science and the functioning of research infrastructure such as CLARIN ERIC.
Accurate opinion mining requires the exact identification of the source and target of an opinion. To evaluate diverse tools, the research community relies on the existence of a gold standard corpus covering this need. Since such a corpus is currently not available for German, the Interest Group on German Sentiment Analysis decided to create such a resource and make it available to the research community in the context of a shared task. In this paper, we describe the selection of textual sources, development of annotation guidelines, and first evaluation results in the creation of a gold standard corpus for the German language.
Machine learning methods offer a great potential to automatically investigate large amounts of data in the humanities. Our contribution to the workshop reports about ongoing work in the BMBF project KobRA (http://www.kobra.tu-dortmund.de) where we apply machine learning methods to the analysis of big corpora in language-focused research of computer-mediated communication (CMC). At the workshop, we will discuss first results from training a Support Vector Machine (SVM) for the classification of selected linguistic features in talk pages of the German Wikipedia corpus in DeReKo provided by the IDS Mannheim. We will investigate different representations of the data to integrate complex syntactic and semantic information for the SVM. The results shall foster both corpus-based research of CMC and the annotation of linguistic features in CMC corpora.
Data Mining with Shallow vs. Linguistic Features to Study Diversification of Scientific Registers
(2014)
We present a methodology to analyze the linguistic evolution of scientific registers with data mining techniques, comparing the insights gained from shallow vs. linguistic features. The focus is on selected scientific disciplines at the boundaries to computer science (computational linguistics, bioinformatics, digital construction, microelectronics). The data basis is the English Scientific Text Corpus (SCITEX) which covers a time range of roughly thirty years (1970/80s to early 2000s) (Degaetano-Ortlieb et al., 2013; Teich and Fankhauser, 2010). In particular, we investigate the diversification of scientific registers over time. Our theoretical basis is Systemic Functional Linguistics (SFL) and its specific incarnation of register theory (Halliday and Hasan, 1985). In terms of methods, we combine corpus-based methods of feature extraction and data mining techniques.