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The workshop presents ATHEN 1 (Annotation and Text Highlighting Environment), an extensible desktop-based annotation environment which supports more than just regular annotation. Besides being a general purpose annotation environment, ATHEN supports indexing and querying support of your data as well as the ability to automatically preprocess your data with Meta information. It is especially suited for those who want to extend existing general purpose annotation tools by implementing their own custom features, which cannot be fulfilled by other available annotation environments. On the according gitlab, we provide online tutorials, which demonstrate the use of specific features of ATHEN
Nonnative accents are prevalent in our globalized world and constitute highly salient cues in social perception. Whereas previous literature has commonly assumed that they cue specific social group stereotypes, we propose that nonnative accents generally trigger spontaneous negatively biased associations (due to a general nonnative accent category and perceptual influences). Accordingly, Study 1 demonstrates negative biases with conceptual IATs, targeting the general concepts of accent versus native speech, on the dimensions affect, trust, and competence, but not on sociability. Study 2 attests to negative, largely enhanced biases on all dimensions with auditory IATs comprising matched native–nonnative speaker pairs for four accent types. Biases emerged irrespective of the accent types that differed in attractiveness, recognizability of origin, and origin-linked national associations. Study 3 replicates general IAT biases with an affect IAT and a conventional evaluative IAT. These findings corroborate our hypotheses and assist in understanding general negativity toward nonnative accents.
Learning new languages has a high relevance in today’s society with a globalized economy and the freedom to move abroad for work, study or other reasons. In this context new methods to teach and learn languages with the help of modern technology are becoming more relevant besides traditional language classes.
This work presents a new approach to combine a traditional language class with a modern computer-based approach for teaching. As a concrete example a web application to help teach and learn Latin was developed.
MULLE is a tool for language learning that focuses on teaching Latin as a foreign language. It is aimed for easy integration into the traditional classroom setting and syllabus, which makes it distinct from other language learning tools that provide standalone learning experience. It uses grammar-based lessons and embraces methods of gamification to improve the learner motivation. The main type of exercise provided by our application is to practice translation, but it is also possible to shift the focus to vocabulary or morphology training.
We present a language learning application that relies on grammars to model the learning outcome. Based on this concept we can provide a powerful framework for language learning exercises with an intuitive user interface and a high reliability. Currently the application aims to augment existing language classes and support students by improving the learner attitude and the general learning outcome. Extensions beyond that scope are promising and likely to be added in the future.
Controlled Natural Languages (CNLs) have many applications including document authoring, automatic reasoning on texts and reliable machine translation, but their application is not limited to these areas. We explore a new application area of CNLs, the use of CNLs in computer-assisted language learning. In this paper we present a a web application for language learning using CNLs as well as a detailed description of the properties of the family of CNLs it uses.
We present WOMBAT, a Python tool which supports NLP practitioners in accessing word embeddings from code. WOMBAT addresses common research problems, including unified access, scaling, and robust and reproducible preprocessing. Code that uses WOMBAT for accessing word embeddings is not only cleaner, more readable, and easier to reuse, but also much more efficient than code using standard in-memory methods: a Python script using WOMBAT for evaluating seven large word embedding collections (8.7M embedding vectors in total) on a simple SemEval sentence similarity task involving 250 raw sentence pairs completes in under ten seconds end-to-end on a standard notebook computer.
Null subjects (NSs) have been a central research topic in generative syntax ever since the 1980s. This chapter considers the situation of German NSs both from a dialectological and from a diachronic perspective and attempts to reconstruct a direct line concerning the licensing conditions of pro-drop from Old High German (OHG) through Middle High German (MHG) and Early New High German (ENHG) to current dialects of New High German (NHG). Particularly, we will argue that German changed from a consistent, yet asymmetric pro-drop language to a partial, but symmetric one. In order to demonstrate that this development took place and the steps involved, we survey the existing empirical evidence and introduce new data.
Since Lerner coined the notion of delayed completion in 1989, this recurrent social practice of continuing one’s speaking turn while disregarding an intermediate co-participant’s utterance has not been investigated with regard to embodied displays and actions. A sequential approach to videotaped mundane conversations in German will explain the occurrence and use of delayed completions. First, especially in multi-party and multi-activity settings, delayed completions can result from reduced monitoring and coordinating activities. Second, recipients can use intra-turn response slots for more extended responsive actions than the current speaker initially projected, leading to delayed completion sequences. Finally, delayed completions are used for blocking possibly misaligned co-participant actions. The investigation of visible action illustrates that delayed completions are a basic practice for retrospectively managing co-participant response slots.