Refine
Year of publication
Document Type
- Part of a Book (20)
- Article (11)
- Conference Proceeding (5)
Has Fulltext
- yes (36)
Keywords
- Computerlinguistik (9)
- Deutsch (7)
- Natürliche Sprache (6)
- Automatische Sprachanalyse (5)
- Konversationsanalyse (4)
- Korpus <Linguistik> (4)
- Annotation (3)
- Interaktion (3)
- Maschinelles Lernen (3)
- Multimodalität (3)
Publicationstate
- Postprint (36) (remove)
Reviewstate
- (Verlags)-Lektorat (17)
- Peer-Review (14)
- Peer-review (1)
Publisher
- Springer (36) (remove)
The English language has taken advantage of the Digital Revolution to establish itself as the global language; however, only 28.6 %of Internet users speak English as their native language. Machine Trans-lation (MT) is a powerful technology that can bridge this gap. In devel-opment since the mid-20th century, MT has become available to every Internet user in the last decade, due to free online MT services. This paper aims to discuss the implications that these tools may have for the privacy of their users and how they are addressed by EU data protec-tion law. It examines the data-flows in respect of the initial processing (both from the perspective of the user and the MT service provider) and potential further processing that may be undertaken by the MT service provider.
Terminological resources play a central role in the organization and retrieval of scientific texts. Both simple keyword lists and advanced modelings 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 elusive theoretical and historical branches, where the use of terminology within documents from different origins is often far from being consistent. In this paper, we report on the progress of a linguistically motivated project on the onomasiological re-modeling of the terminological resources for the grammatical information system grammis. We present the design principles and the results of their application. In particular, we focus on new features for the authoring backend and discuss how these innovations help to evaluate existing, loosely structured terminological content, as well as to efficiently deal with automatic term extraction. Furthermore, we introduce a transformation to a future SKOS representation. We conclude with a positioning of our resources with regard to the Knowledge Organization discourse and discuss how a highly complex information environment like grammis benefits from the re-designed terminological KOS.
The paper presents the results of a joint effort of a group of multimodality researchers and tool developers to improve the interoperability between several tools used for the annotation and analysis of multimodality. Each of the tools has specific strengths so that a variety of different tools, working on the same data, can be desirable for project work. However this usually requires tedious conversion between formats. We propose a common exchange format for multimodal annotation, based on the annotation graph (AG) formalism, which is supported by import and export routines in the respective tools. In the current version of this format the common denominator information can be reliably exchanged between the tools, and additional information can be stored in a standardized way.
Just like most varieties of West Germanic, virtually all varieties of German use a construction in which a cognate of the English verb 'do' (standard German 'tun') functions as an auxiliary and selects another verb in the bare infinitive, a construction known as 'do'-periphrasis or 'do'-support. The present paper provides an Optimality Theoretic (OT) analysis of this phenomenon. It builds on a previous analysis by Bader and Schmid (An OT-analysis of 'do'-support in Modern German, 2006) but (i) extends it from root clauses to subordinate clauses and (ii) aims to capture all of the major distributional patterns found across (mostly non-standard) varieties of German. In so doing, the data are used as a testing ground for different models of German clause structure. At first sight, the occurrence of 'do' in subordinate clauses, as found in many varieties, appears to support the standard CP-IP-VP analysis of German. In actual fact, however, the full range of data turn out to challenge, rather than support, this model. Instead, I propose an analysis within the IP-less model by Haider (Deutsche Syntax - generativ. Vorstudien zur Theorie einer projektiven Grammatik, Narr, Tübingen, 1993 et seq.). In sum, the 'do'-support data will be shown to have implications not only for the analysis of clause structure but also for the OT constraints commonly assumed to govern the distribution of 'do', for the theory of non-projecting words (Toivonen in Non-projecting words, Kluwer, Dordrecht, 2003) as well as research on grammaticalization.
Although there is a growing interest of policy makers in higher education issues (especially on an international scale), there is still a lack of theoretically well-grounded comparative analyses of higher education policy. Even broadly discussed topics in higher education research like the potential convergence of European higher education systems in the course of the Bologna Process suffer from a thin empirical and comparative basis. This paper aims to deal with these problems by addressing theoretical questions concerning the domestic impact of the Bologna Process and the role national factors play in determining its effects on cross-national policy convergence. It develops a distinct theoretical approach for the systematic and comparative analysis of cross-national policy convergence. In doing so, it relies upon insights from related research areas — namely literature on Europeanization as well as studies dealing with cross-national policy convergence.
In this article, we examine the effectiveness of bootstrapping supervised machine-learning polarity classifiers with the help of a domain-independent rule-based classifier that relies on a lexical resource, i.e., a polarity lexicon and a set of linguistic rules. The benefit of this method is that though no labeled training data are required, it allows a classifier to capture in-domain knowledge by training a supervised classifier with in-domain features, such as bag of words, on instances labeled by a rule-based classifier. Thus, this approach can be considered as a simple and effective method for domain adaptation. Among the list of components of this approach, we investigate how important the quality of the rule-based classifier is and what features are useful for the supervised classifier. In particular, the former addresses the issue in how far linguistic modeling is relevant for this task. We not only examine how this method performs under more difficult settings in which classes are not balanced and mixed reviews are included in the data set but also compare how this linguistically-driven method relates to state-of-the-art statistical domain adaptation.
This paper deals with different views of lexical semantics. The focus is on the relationship between lexical expressions and conceptual components. First the assumptions about lexicalization and decompositionality of concepts shared by the most semanticists are presented, followed by a discussion of the differences between two-level-semantics and one-level-semantics. The final part is concentrated on the interpretation of conceptual components in situations of communication.
We present a method to identify and document a phenomenon on which there is very little empirical data: German phrasal compounds occurring in the form of as a single token (without punctuation between their components). Relying on linguistic criteria, our approach implies to have an operational notion of compounds which can be systematically applied as well as (web) corpora which are large and diverse enough to contain rarely seen phenomena. The method is based on word segmentation and morphological analysis, it takes advantage of a data-driven learning process. Our results show that coarse-grained identification of phrasal compounds is best performed with empirical data, whereas fine-grained detection could be improved with a combination of rule-based and frequency-based word lists. Along with the characteristics of web texts, the orthographic realizations seem to be linked to the degree of expressivity.
Contemporary studies on the characteristics of natural language benefit enormously from the increasing amount of linguistic corpora. Aside from text and speech corpora, corpora of computer-mediated communication (CMC) Position themselves between orality and literacy, and beyond that provide in- sight into the impact of "new", mainly intemet-based media on language beha- viour. In this paper, we present an empirical attempt to work with annotated CMC corpora for the explanation of linguistic phenomena. In concrete terms, we implement machine leaming algorithms to produce decision trees that reveal rules and tendencies about the use of genitive markers in German.