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This paper will address the challenge of creating a knowledge graph from a corpus of historical encyclopedias with a special focus on word sense alignment (WSA) and disambiguation (WSD). More precisely, we examine WSA and WSD approaches based on article similarity to link messy historical data, utilizing Wikipedia as aground-truth component – as the lack of a critical overlap in content paired with the amount of variation between and within the encyclopedias does not allow for choosing a ”baseline” encyclopedia to align the others to. Additionally, we are comparing the disambiguation performance of conservative methods like the Lesk algorithm to more recent approaches, i.e. using language models to disambiguate senses.
Who is we? Disambiguating the referents of first person plural pronouns in parliamentary debates
(2021)
This paper investigates the use of first person plural pronouns as a rhetorical device in political speeches. We present an annotation schema for disambiguating pronoun references and use our schema to create an annotated corpus of debates from the German Bundestag. We then use our corpus to learn to automatically resolve pronoun referents in parliamentary debates. We explore the use of data augmentation with weak supervision to further expand our corpus and report preliminary results.
N-grams are of utmost importance for modern linguistics and language technology. The legal status of n-grams, however, raises many practical questions. Traditionally, text snippets are considered copyrightable if they meet the originality criterion, but no clear indicators as to the minimum length of original snippets exist; moreover, the solutions adopted in some EU Member States (the paper cites German and French law as examples) are considerably different. Furthermore, recent developments in EU law (the CJEU's Pelham decision and the new right of press publishers) also provide interesting arguments in this debate. The paper presents the existing approaches to the legal protection of n-grams and tries to formulate some clear guidelines as to the length of n-grams that can be freely used and shared.
Der Beitrag beschreibt die Motivation und Ziele des Europäischen Referenzkorpus EuReCo, einer offenen Initiative, die darauf abzielt, dynamisch definierbare virtuelle vergleichbare Korpora auf der Grundlage bestehender nationaler, Referenz- oder anderer großer Korpora bereitzustellen und zu verwenden. Angesichts der bekannten Unzulänglichkeiten anderer Arten mehrsprachiger Korpora wie Parallel- bzw. Übersetzungskorpora oder rein webbasierte vergleichbare Korpora, stellt das EuReCo eine einzigartige linguistische Ressource dar, die neue Perspektiven für germanistische und vergleichende wie angewandte Korpuslinguistik, insbesondere im europäischen Kontext, eröffnet.
The German e-dictionary documenting confusables Paronyme – Dynamisch im Kontrast contains lexemes which are similar in sound, spelling and/or meaning, e.g. autoritär/autoritativ, innovativ/innovatorisch. These can cause uncertainty as to their appropriate use. The monolingual guide could be easily expanded to become a multilingual platform for commonly confused items by incorporating language modules. The value of this visionary resource is manifold. Firstly, e-dictionaries of confusables have not yet been compiled for most European languages; consequently, the German resource could serve as a model of practice. Secondly, it would be able to explain the usage of false friends. Thirdly, cognates and loan word equivalents would be offered for simultaneous consultation. Fourthly, users could find out whether, for example, a German pair is semantically equivalent to a pair in another language. Finally, it would inform users about cases where a pair of semantically similar words in one language has only one lexical counterpart in another language. This paper is an appeal for visionary projects and collaborative enterprises. I will outline the dictionary’s layout and contents as shown by its contrastive entries. I will demonstrate potential additions, which would make it possible to build up a large platform for easily misused words in different languages.
Validating the Performativity Hypothesis to Neg-Raising using corpus data: Evidence from Polish
(2021)
Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus
(2021)
Since the introduction of large language models in Natural Language Processing, large raw corpora have played a crucial role in Computational Linguistics. However, most of these large raw corpora are either available only for English or not available to the general public due to copyright issues. Nevertheless, there are some examples of freely available multilingual corpora for training Deep Learning NLP models, such as the OSCAR and Paracrawl corpora. However, they have quality issues, especially for low-resource languages. Moreover, recreating or updating these corpora is very complex. In this work, we try to reproduce and improve the goclassy pipeline used to create the OSCAR corpus. We propose a new pipeline that is faster, modular, parameterizable, and well documented. We use it to create a corpus similar to OSCAR but larger and based on recent data. Also, unlike OSCAR, the metadata information is at the document level. We release our pipeline under an open source license and publish the corpus under a research-only license.
Towards comprehensive definitions of data quality for audiovisual annotated language resources
(2021)
Though digital infrastructures such as CLARIN have been successfully established and now provide large collections of digital resources, the lack of widely accepted standards for data quality and documentation still makes re-use of research data a difficult endeavour, especially for more complex resource types. The article gives a detailed overview over relevant characteristics of audiovisual annotated language resources and reviews possible approaches to data quality in terms of their suitability for the current context. Conclusively, various strategies are suggested in order to arrive at comprehensive and adequate definitions of data quality for this specific resource type and possibly for digital language resources in general.
Die Sprache von Lerner/-innen einer Fremdsprache unterscheidet sich auf allen linguistischen Ebenen von der Sprache von Muttersprachler/-innen. Seit einigen Jahrzehnten werden Lernerkorpora gebaut, um Lernersprache quantitativ und qualitativ zu analysieren. Hier argumentieren wir anhand von drei Fallbeispielen (zu Modifikation, Koselektion und rhetorischen Strukturen) für eine linguistisch informierte, tiefe Phänomenmodellierung und Annotation sowie für eine auf das jeweilige Phänomen passende formale und quantitative Modellierung. Dabei diskutieren wir die Abwägung von tiefer, mehrschichtiger Analyse einerseits und notwendigen Datenmengen für bestimmte quantitative Verfahren andererseits und zeigen, dass mittelgroße Korpora (wie die meisten Lernerkorpora) interessante Erkenntnisse ermöglichen, die große, flacher annotierte Korpora so nicht erlauben würden.