410 Linguistik
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The compilation of terminological vocabularies plays a central role in the organization and retrieval of scientific texts. Both simple keyword lists as well as sophisticated modellings of relationships between terminological concepts can make a most valuable contribution to the analysis, classification, and finding of appropriate digital documents, either on the Web or within local repositories. This seems especially true for long-established scientific fields with various theoretical and historical branches, such as linguistics, where the use of terminology within documents from different origins is sometimes far from being consistent. In this short paper, we report on the early stages of a project that aims at the re-design of an existing domain-specific KOS for grammatical content grammis. In particular, we deal with the terminological part of grammis and present the state-of-the-art of this online resource as well as the key re-design principles. Further, we propose questions regarding ramifications of the Linked Open Data and Semantic Web approaches for our re-design decisions.
In this paper, we describe preliminary results from an ongoing experiment wherein we classify two large unstructured text corpora—a web corpus and a newspaper corpus—by topic domain (or subject area). Our primary goal is to develop a method that allows for the reliable annotation of large crawled web corpora with meta data required by many corpus linguists. We are especially interested in designing an annotation scheme whose categories are both intuitively interpretable by linguists and firmly rooted in the distribution of lexical material in the documents. Since we use data from a web corpus and a more traditional corpus, we also contribute to the important field of corpus comparison and corpus evaluation. Technically, we use (unsupervised) topic modeling to automatically induce topic distributions over gold standard corpora that were manually annotated for 13 coarse-grained topic domains. In a second step, we apply supervised machine learning to learn the manually annotated topic domains using the previously induced topics as features. We achieve around 70% accuracy in 10-fold cross validations. An analysis of the errors clearly indicates, however, that a revised classification scheme and larger gold standard corpora will likely lead to a substantial increase in accuracy.
This paper introduces the recently started DRuKoLA-project that aims at providing mechanisms to flexibly draw virtual comparable corpora from the German Reference Corpus DeReKo and the Reference Corpus of Contemporary Romanian Language CoRoLa in order to use these virtual corpora as empirical basis for contrastive linguistic research.
This paper presents our model of ‘MultiWord Patterns’ (MWPs). MWPs are defined as recurrent frozen schemes with fixed lexical components and productive slots that have a holistic – but not necessarily idiomatic – meaning and/or function, sometimes only on an abstract level. These patterns can only be reconstructed with corpus-driven, iterative (qualitative-quantitative) methods. This methodology includes complex phrase searches, collocation analysis that not only detects significant word pairs, but also significant syntagmatic cotext patterns and slot analysis with our UWV Tool. This tool allows us to bundle KWICs in order to detect the nature of lexical fillers for and to visualize MWP hierarchies.
This contribution presents the background, design and results of a study of users of three oral corpus platforms in Germany. Roughly 5.000 registered users of the Database for Spoken German (DGD), the GeWiss corpus and the corpora of the Hamburg Centre for Language Corpora (HZSK) were asked to participate in a user survey. This quantitative approach was complemented by qualitative interviews with selected users. We briefly introduce the corpus resources involved in the study in section 2. Section 3 describes the methods employed in the user studies. Section 4 summarizes results of the studies focusing on selected key topics. Section 5 attempts a generalization of these results to larger contexts.
Sense relations
(2016)
Constructing a Corpus
(2016)
Researchers in Natural Language Processing rely on availability of data and software, ideally under open licenses, but little is done to actively encourage it. In fact, the current Copyright framework grants exclusive rights to authors to copy their works, make them available to the public and make derivative works (such as annotated language corpora). Moreover, in the EU databases are protected against unauthorized extraction and re-utilization of their contents. Therefore, proper public licensing plays a crucial role in providing access to research data. A public license is a license that grants certain rights not to one particular user, but to the general public (everybody). Our article presents a tool that we developed and whose purpose is to assist the user in the licensing process. As software and data should be licensed under different licenses, the tool is composed of two separate parts: Data and Software. The underlying logic as well as elements of the graphic interface are presented below.
In order to develop its full potential, global communication needs linguistic support systems such as Machine Translation (MT). In the past decade, free online MT tools have become available to the general public, and the quality of their output is increasing. However, the use of such tools may entail various legal implications, especially as far as processing of personal data is concerned. This is even more evident if we take into account that their business model is largely based on providing translation in exchange for data, which can subsequently be used to improve the translation model, but also for commercial purposes. The purpose of this paper is to examine how free online MT tools fit in the European data protection framework, harmonised by the EU Data Protection Directive. The perspectives of both the user and the MT service provider are taken into account.