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Datensatz Schwache Maskulina
(2023)
Der Datensatz enthält eine Sammlung von 1.156 Substantiven (mit wenigen Ausnahmen Maskulina), die sich im Korpusgrammatik-Untersuchungskorpus (Bubenhofer et al. 2014), basierend auf dem Deutschen Referenzkorpus DeReKo (Kupietz et al. 2010, 2018), Release 2017-II, unmittelbar nach einem Beleg für die Akkusativ- oder Dativform des unbestimmten Artikels ( einen / einem ) mindestens einmal mit der “schwachen” Endung -(e)n belegen lassen (z.B. einen Aktivisten , einem Autoren ). Einzelheiten zur Datenerhebung in Weber & Hansen (2023).
Annotated dataset consisting of personal designations found on websites of 42 German, Austrian, Swiss and South Tyrolean cities. Our goal is to re-evaluate the websites every year in order to see how the use of gender-fair language develops over time. The dataset contains coordinates for the creation of map material.
In order to differentiate between figurative and literal usage of verb-noun combinations for the shared task on the disambiguation of German Verbal Idioms issued for KONVENS 2021, we apply and extend an approach originally developed for detecting idioms in a dataset consisting of random ngram samples. The classification is done by implementing a rather shallow, statistics-based pipeline without intensive preprocessing and examinations on the morphosyntactic and semantic level. We describe the overall approach, the differences between the original dataset and the dataset of the KONVENS task, provide experimental classification results, and analyse the individual contributions of our feature sets.
Der Datensatz enthält 10.113 Korpusbelege für Konstruktionen, in denen ein Substantiv mit einem dass-Satz oder einem zu-Infinitiv auftritt (das Versprechen, dass man sich irgendwann wiedersieht vs. das Versprechen, sich irgendwann wiederzusehen).
Die Daten wurden erhoben aus:
1. dem Korpusgrammatik-Untersuchungskorpus (Bubenhofer et al. 2014), basierend auf dem Deutschen Referenzkorpus DeReKo (Kupietz et al. 2010, 2018), Release 2017-II.
2. dem Subkorpus “Forum” des DECOW16B-Webkorpus (Schäfer & Bildhauer 2012).
This article details the process of creating the Nottinghamer Korpus deutscher YouTube-Sprache ('The Nottingham German YouTube Language Corpus' - or NottDeuYTSch corpus) and outlines potential research opportunities. The corpus was compiled to analyse the online language produced by young German-speakers and offers significant opportunity for in-depth research across several linguistic fields including lexis, morphology, syntax, orthography, and conversational and discursive analysis. The NottDeuYTSch corpus contains over 33 million words taken from approximately 3 million YouTube comments from videos published between 2008 to 2018 targeted at a young, German-speaking demographic and represent an authentic language snapshot of young German speakers. The corpus was proportionally sampled based on video category and year from a database of 112 popular German-speaking YouTube channels in the DACH region for optimal representativeness and balance and contains a considerable amount of associated metadata for each comment that enable further longitudinal cross-sectional analyses. The NottDeuYTSch corpus is available for analysis as part of the German Reference Corpus (DeReKo).
We investigate the optional omission of the infinitival marker in a Swedish future tense construction. During the last two decades the frequency of omission has been rapidly increasing, and this process has received considerable attention in the literature. We test whether the knowledge which has been accumulated can yield accurate predictions of language variation and change. We extracted all occurrences of the construction from a very large collection of corpora. The dataset was automatically annotated with language-internal predictors which have previously been shown or hypothesized to affect the variation. We trained several models in order to make two kinds of predictions: whether the marker will be omitted in a specific utterance and how large the proportion of omissions will be for a given time period. For most of the approaches we tried, we were not able to achieve a better-than-baseline performance. The only exception was predicting the proportion of omissions using autoregressive integrated moving average models for one-step-ahead forecast, and in this case time was the only predictor that mattered. Our data suggest that most of the language-internal predictors do have some effect on the variation, but the effect is not strong enough to yield reliable predictions.
The QUEST (QUality ESTablished) project aims at ensuring the reusability of audio-visual datasets (Wamprechtshammer et al., 2022) by devising quality criteria and curating processes. RefCo (Reference Corpora) is an initiative within QUEST in collaboration with DoReCo (Documentation Reference Corpus, Paschen et al. (2020)) focusing on language documentation projects. Previously, Aznar and Seifart (2020) introduced a set of quality criteria dedicated to documenting fieldwork corpora. Based on these criteria, we establish a semi-automatic review process for existing and work-in-progress corpora, in particular for language documentation. The goal is to improve the quality of a corpus by increasing its reusability. A central part of this process is a template for machine-readable corpus documentation and automatic data verification based on this documentation. In addition to the documentation and automatic verification, the process involves a human review and potentially results in a RefCo certification of the corpus. For each of these steps, we provide guidelines and manuals. We describe the evaluation process in detail, highlight the current limits for automatic evaluation and how the manual review is organized accordingly.
Metadata provides important information relevant both to finding and understanding corpus data. Meaningful linguistic data requires both reasonable annotations and documentation of these annotations. This documentation is part of the metadata of a dataset. While corpus documentation has often been provided in the form of accompanying publications, machinereadable metadata, both containing the bibliographic information and documenting the corpus data, has many advantages. Metadata standards allow for the development of common tools and interfaces. In this paper I want to add a new perspective from an archive’s point of view and look at the metadata provided for four learner corpora and discuss the suitability of established standards for machine-readable metadata. I am are aware that there is ongoing work towards metadata standards for learner corpora. However, I would like to keep the discussion going and add another point of view: increasing findability and reusability of learner corpora in an archiving context.
We address the task of distinguishing implicitly abusive sentences on identity groups (“Muslims contaminate our planet”) from other group-related negative polar sentences (“Muslims despise terrorism”). Implicitly abusive language are utterances not conveyed by abusive words (e.g. “bimbo” or “scum”). So far, the detection of such utterances could not be properly addressed since existing datasets displaying a high degree of implicit abuse are fairly biased. Following the recently-proposed strategy to solve implicit abuse by separately addressing its different subtypes, we present a new focused and less biased dataset that consists of the subtype of atomic negative sentences about identity groups. For that task, we model components that each address one facet of such implicit abuse, i.e. depiction as perpetrators, aspectual classification and non-conformist views. The approach generalizes across different identity groups and languages.