University of Hildesheim
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Data Exploration of Sentence Structures and Embellishments in German texts: Comparing Children’s Writing vs Literature
No full textIt is of interest to study sentence construction for children’s writing in order to understand grammatical errors and their influence on didactic decisions. For this purpose, this paper analyses sentence structures for various age groups of children’s writings in contrast to text taken from children’s and youth literature. While valency differs little between text type and age group, sentence embellishments show some differences. Both use of adjectives and adverbs increase with age and book levels. Furthermore books show a larger use thereof. This work presents one of the steps in a larger ongoing effort to understand children’s writing and reading competences at word and sentence level. The need to look at variable from non-variable features of sentence structures separately in order to find distinctive features has been an important finding
Resources, Tools, and Applications at the CLARIN Center Stuttgart
No full textThis NECTAR track paper (NECTAR: new scientific and technical advances in research) summarizes recent research and curation activities at the CLARIN center Stuttgart. CLARIN is a European initiative to advance research in humanities and social sciences by providing language-based resources via a shared distributed infrastructure. We provide an overview of the resources (i.e., corpora, lexical resources, and tools) hosted at the IMS Stuttgart that are available through CLARIN and show how to access them. For illustration, we present two examples of the integration of various resources into Digital Humanities projects. We conclude with a brief outlook on the future challenges in the Digital Humanities
Saarland University’s Participation in the GErman SenTiment AnaLysis shared Task (GESTALT)
No full textWe report on the two systems we built for Task 1 of the German Sentiment Analysis Shared Task, the task on Source, Subjective Expression and Target Extraction from Political Speeches (STEPS). The first system is a rule-based system relying on a predicate lexicon specifying extraction rules for verbs, nouns and adjectives, while the second is a translation-based system that has been obtained with the help of the (English) MPQA corpus
Leichte Sprache und der Übersetzungsaspekt Lassen sich Fachtexte in Leichte Sprache übersetzen? Ist ein Leichte-Sprache-Text überhaupt eine Übersetzung?
No full textDas Gesetz zur Gleichstellung behinderter Menschen sieht u. a. vor, dass hör- und sprachbehinderte Menschen das Recht haben, die Deutsche Gebärdensprache oder andere geeignete Kommunikationshilfen zu verwenden. Leichte Sprache kann als solch eine geeignete Kommunikationshilfe angesehen werden. In dieser Ausarbeitung liegt der Fokus auf der Kommunikation zwischen Behörden/Ämtern und Bürgern und auf den Herausforderungen, die diese mit sich bringt. „Beamtendeutsch“ ist bereits für die meisten Laien schwer verständlich. Folglich stellt es für Menschen, die über eine geringere Sprachkompetenz verfügen eine noch größere Schwierigkeit dar mit einer Behörde/einem Amt erfolgreich zu kommunizieren. Leichte Sprache kann hier als Kommunikationshilfe eingesetzt werden. Die Forschungsfrage dieser Arbeit lautet: Wie gut und auf welche Art lassen sich Texte, die durch die Fachsprache der Verwaltung gekennzeichnet sind, in Leichte Sprache übertragen? Des Weiteren wird hinterfragt, ob es sich bei Texten in Leichter Sprache immer um eine Übersetzung handelt oder ob sie vielmehr als Neufassungen zu betrachten sind. Das untersuchte Korpus besteht aus einem Formular des Niedersächsischen Justizministeriums und der dazugehörigen Ausfüllhilfe in Leichter Sprache
GermEval-2014: Nested Named Entity Recognition with Neural Networks
No full textCollobert et al. (2011) showed that deep neural network architectures achieve state- of-the-art performance in many fundamental NLP tasks, including Named Entity Recognition (NER). However, results were only reported for English. This paper reports on experiments for German Named Entity Recognition, using the data from the GermEval 2014 shared task on NER. Our system achieves an F1 -measure of 75.09% according to the official metric
Semi-Supervised Neural Networks for Nested Named Entity Recognition
No full textIn this paper, we investigate a semi- supervised learning approach based on neu- ral networks for nested named entity recog- nition on the GermEval 2014 dataset. The dataset consists of triples of a word, a named entity associated with that word in the first-level and one in the second-level. Additionally, the tag distribution is highly skewed, that is, the number of occurrences of certain types of tags is too small. Hence, we present a unified neural network archi- tecture to deal with named entities in both levels simultaneously and to improve gen- eralization performance on the classes that have a small number of labelled examples
BECREATIVE: Annotation of German Named Entities
No full textThis paper presents the BECREATIVE Named Entity Recognition system and its participation at the GermEval 2014 Named Entity Recognition Shared Task (Benikova et al., 2014a). BECREATIVE uses a hybrid approach of two commonly used procedural methods, namely list-based lookups and machine learning (Naive Bayes Classification), which centers around the classifier. BECREATIVE currently reaches an F-score of 37.34 on the strict evaluation setting applied on the development set provided by GermEval
Dietrich von Stechow, Bischof von Brandenburg 1459–1472. Regesten zur Vita und zum Episkopat
No full textFür diesen Beitrag wurden alle bekannten Erwähnungen Bischof Dietrichs von Brandenburg aus der zeitgenössischen Überlieferung zusammengetragen. Diese geben vor allem Aufschluss über Dietrichs Tätigkeit als Dompropst und Bischof von Brandenburg sowie das Verhältnis Dietrichs zu den Kurfürsten Friedrich II. (1440-1470) und Albrecht Achilles (1470-1486) von Brandenburg
Nessy: A Hybrid Approach to Named Entity Recognition for German
No full textIn this paper we present Nessy (Named Entity Searching System) and its application to German in the context of the GermEval 2014 Named Entity Recognition Shared Task (Benikova et al., 2014a). We tackle the challenge by using a combination of machine learning (Naive Bayes classification) and rule-based methods. Altogether, Nessy achieves an F-score of 58.78% on the final test set
Grammatical gender in the discourse of multilingual children's acquisition of German
No full textThe acquisition of grammatical gender by multilingual pre-school children (aged six) was investigated by observing their narration and discourse. It emerged that only three of the 17 children actually used gender to classify nouns. Grammatical agreement is acknowledged as a key feature of gender acquisition, and it reflects developmental steps. Children growing up with mostly bilingual German input at a low proficiency level had the greatest difficulties in acquiring gender and agreement in the group investigated