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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.