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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.
The aim of this work is to describe criteria used in the process of inclusion and treatment of neologisms in dictionaries of Spanish within the framework of pandemic instability. Our starting point will be data obtained by the Antenas Neológicas Network (https://www.upf.edu/web/antenas), whose representation in three different lexicographic tools will be analyzed with the purpose of identifying problems in the methodology used to dictionarize – that is, how and what words were selected to be included in dictionaries and how they were represented in their entries – neologisms during the COVID-19 pandemic (sources and corpora of analysis, selection criteria, types of definition, among other aspects). Two of them are monolingual and COVID-19 lexical units were included as part of their updates: the Antenario, a dictionary of neologisms of Spanish varieties, and the Diccionario de la Lengua Española [DLE], a dictionary of general Spanish, published by the Real Academia Española [RAE], Spanish Royal Academy). The other is a bilingual unidirectional English-Spanish dictionary first published as a glossary, Diccionario de COVID-19 EN-ES [TREMEDICA], entirely made up of neological and non-neological lexical units related to the virus and the pandemic. Thus, the target lexis was either included in existing works or makes up the whole of a new tool located in a portal together with other lexicographic tools. Unlike other collections of COVID-19 vocabulary that kept cropping up as the pandemic unfolded, all three have been designed and written according to well-established lexicographic practices.
Our working hypothesis is that the need to record and define words which were recently created impacts the criteria for inclusion and treatment of neologisms in dictionaries about Spanish, including a certain degree of overlap of some features which are traditionally thought to be specific to each type of dictionary.
The annual microcensus provides Germany’s most important official statistics. Unlike a census it does not cover the whole population, but a representative 1%-sample of it. In 2017, the German microcensus asked a question on the language of the population, i.e. ‘Which language is mainly spoken in your household?’ Unfortunately, the question, its design and its position within the whole microcensus’ questionnaire feature several shortcomings. The main shortcoming is that multilingual repertoires cannot be captured by it. Recommendations for the improvement of the microcensus’ language question: first and foremost the question (i.e. its wording, design, and answer options) should make it possible to count multilingual repertoires.
This paper explores how attitudes affect the seemingly objective process of counting speakers of varieties using the example of Low German, Germany’s sole regional language. The initial focus is on the basic taxonomy of classifying a variety as a language or a dialect. Three representative surveys then provide data for the analysis: the Germany Survey 2008, the Northern Germany Survey 2016, and the Germany Survey 2017. The results of these surveys indicate that there is no consensus concerning the evaluation of Low German’s status and that attitudes towards Low German are related to, for example, proficiency in the language. These attitudes are shown to matter when counting speakers of Low German and investigating the status it has been accorded.
Language attitudes matter; they influence people’s behaviour and decisions. Therefore, it is crucial to learn more about patterns in the way that languages are evaluated. One means of doing so is using a quantitative approach with data representative of a whole population, so that results mirror dispositions at a societal level. This kind of approach is adopted here, with a focus on the situation in Germany. The article consists of two parts. First, I will present some results of a new representative survey on language attitudes in Germany (the Germany Survey 2017). Second, I will show how language attitudes penetrate even seemingly objective data collection processes by examining the German Microcensus. In 2017, for the first time in eighty years, the German Microcensus included a question on language use ‘at home’. Unfortunately, however, the question was clearly tainted by language attitudes instead of being objective. As a result, the Microcensus significantly misrepresents the linguistic reality of different migrant languages spoken in Germany.
Germany's (single) national official language is German. The dominance of German in schools, politics, the legal system, administration and the entire written public domain is so great that for a long time the lack of a coherent language policy was not seen as a problem. State restraint in this area is due, on the one hand, to historical reasons; on the other hand, it has been promoted by the federal system in Germany, which grants the federal states far-reaching responsibilities in the fields of education and culture. More recently, multilingualism among the population has increased and has resulted in a growing interest in understanding the language situation in Germany and (in particular) taking a closer look at the different minority languages. In 2017, for the first time in about 80 years, there is a question on the language of the population in the German micro census. The Institute for the German Language has also carried out various representative surveys; in the winter of 2017/201, a large representative survey with questions on the language repertoire and language attitudes is in the field.
Who understands Low German today and who can speak it? Who makes use of media and cultural events in Low German? What images do people in northern Germany associate with Low German and what is their view of their regional language?
These and further questions are answered in this brochure with the help of representative data collected in a telephone survey of a total of 1,632 people from eight federal states (Bremen, Hamburg, Lower Saxony, Mecklenburg-West Pomerania and Schleswig-Holstein as well as Brandenburg, North Rhine-Westphalia and Saxony-Anhalt).
This paper outlines the generation process of a specifi computational linguistic representation termed the Multilingual Time Map, conceptually a multi-tape finit state transducer encoding linguistic data at different levels of granularity. The fi st component acquires phonological data from syllable labeled speech data, the second component define feature profiles the third component generates feature hierarchies and augments the acquired data with the define feature profiles and the fourth component displays the Multilingual Time Map as a graph.
Although the N400 was originally discovered in a paradigm designed to elicit a P300 (Kutas and Hillyard, 1980), its relationship with the P300 and how both overlapping event-related potentials (ERPs) determine behavioral profiles is still elusive. Here we conducted an ERP (N = 20) and a multiple-response speed-accuracy tradeoff (SAT) experiment (N = 16) on distinct participant samples using an antonym paradigm (The opposite of black is white/nice/yellow with acceptability judgment). We hypothesized that SAT profiles incorporate processes of task-related decision-making (P300) and stimulus-related expectation violation (N400). We replicated previous ERP results (Roehm et al., 2007): in the correct condition (white), the expected target elicits a P300, while both expectation violations engender an N400 [reduced for related (yellow) vs. unrelated targets (nice)]. Using multivariate Bayesian mixed-effects models, we modeled the P300 and N400 responses simultaneously and found that correlation between residuals and subject-level random effects of each response window was minimal, suggesting that the components are largely independent. For the SAT data, we found that antonyms and unrelated targets had a similar slope (rate of increase in accuracy over time) and an asymptote at ceiling, while related targets showed both a lower slope and a lower asymptote, reaching only approximately 80% accuracy. Using a GLMM-based approach (Davidson and Martin, 2013), we modeled these dynamics using response time and condition as predictors. Replacing the predictor for condition with the averaged P300 and N400 amplitudes from the ERP experiment, we achieved identical model performance. We then examined the piecewise contribution of the P300 and N400 amplitudes with partial effects (see Hohenstein and Kliegl, 2015). Unsurprisingly, the P300 amplitude was the strongest contributor to the SAT-curve in the antonym condition and the N400 was the strongest contributor in the unrelated condition. In brief, this is the first demonstration of how overlapping ERP responses in one sample of participants predict behavioral SAT profiles of another sample. The P300 and N400 reflect two independent but interacting processes and the competition between these processes is reflected differently in behavioral parameters of speed and accuracy.
In this paper we examine the composition and interactional deployment of suspended assessments in ordinary German conversation. We define suspended assessments as lexicosyntactically incomplete assessing TCUs that share a distinct cluster of prosodic-phonetic features which auditorily makes them come off as 'left hanging' rather than cut-off (e.g., Schegloff/Jefferson/Sacks 1977; Jasperson 2002) or trailing-off (e.g., Local/Kelly 1986; Walker 2012). Using CA/IL methodology (Couper-Kuhlen/Selting 2018) and drawing on a large body of video-recorded face-to-face conversations, we highlight the verbal, vocal and bodily-visual resources participants use to render such unfinished assessing TCUs recognizably incomplete and identify six recurrent usage types. Overall, the suspension of assessing TCUs appears to either serve as a practice for circumventing the production of assessments that are interactionally inapposite, or as a practice for coping with local contingencies that render the very doing of an assessment problematic for the speaker. Data are in German with English translations.
Preface
(2019)
Preface
(2020)
Physicists look at language
(2006)
This paper aims at verifying if the most important online Brazilian Portuguese dictionaries include some of the neologisms identified in texts published in the 1990s to 2000s, formed with the elements ciber-, e-, bio-, eco- and narco, which we refer to as fractomorphemes / fracto-morphèmes. Three online dictionaries were analyzed (Aulete, Houaiss and Michaelis), as well as Vocabulário Ortográfico da Língua Portuguesa (VOLP). We were able to conclude that all three dictionaries and VOLP include neologisms with these elements; Michaelis and VOLP do not include separate entries for bound morphemes, whereas Houaiss includes entries for all of them and Aulete includes entries for bio-, eco- and narco-. Aulete also describes the neological meaning of eco- and narco-, whereas Houaiss does not.
This White Paper sets out commonly agreed definitions on activities of consortia within NFDI. It aims to provide a common basis for reporting and reference regarding selected questions of cross-consortial relevance in DFG’s template for the Interim Reports. The questions were prioritised by an NFDI Task Force on Evaluation and Reporting (formerly Task Force Monitoring) as a result of discussing possible answers to the DFG template. In this process the need to agree on a generalizable meaning of terms commonly used in the context of NFDI, and reporting in particular, were identified from cross-consortial perspectives. Questions that showed the highest requirement on clarification are discussed in this White Paper. As NFDI evolves, the Task Force will likely propose further joint approaches for reporting in information infrastructures.
While each of broad relevance, the questions addressed relate to substantially different aspects of consortia’s work. They are thus also structured slightly different.
Collaborative work in NFDI
(2023)
The non-profit association National Research Data Infrastructure (NFDI) promotes science and research through a National Research Data Infrastructure. Its aim is to develop and establish an overarching research data management (RDM) for Germany and to increase the efficiency of the entire German science system. After a two-and-a-half year build up phase, the process of adding new consortia, each representing a different data domain, has ended in March 2023. NFDI now has 26 disciplinary consortia (and one additional basic service collaboration). Now the full extent of cross-consortial interaction is beginning to show.
The automatic recognition of idioms poses a challenging problem for NLP applications. Whereas native speakers can intuitively handle multiword expressions whose compositional meanings are hard to trace back to individual word semantics, there is still ample scope for improvement regarding computational approaches. We assume that idiomatic constructions can be characterized by gradual intensities of semantic non-compositionality, formal fixedness, and unusual usage context, and introduce a number of measures for these characteristics, comprising count-based and predictive collocation measures together with measures of context (un)similarity. We evaluate our approach on a manually labelled gold standard, derived from a corpus of German pop lyrics. To this end, we apply a Random Forest classifier to analyze the individual contribution of features for automatically detecting idioms, and study the trade-off between recall and precision. Finally, we evaluate the classifier on an independent dataset of idioms extracted from a list of Wikipedia idioms, achieving state-of-the art accuracy.