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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.
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.
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 study investigates cross-language differences in pitch range and variation in four languages from two language groups: English and German (Germanic) and Bulgarian and Polish (Slavic). The analysis is based on large multi-speaker corpora (48 speakers for Polish, 60 for each of the other three languages). Linear mixed models were computed that include various distributional measures of pitch level, span and variation, revealing characteristic differences across languages and between language groups. A classification experiment based on the relevant parameter measures (span, kurtosis and skewness values for pitch distributions for each speaker) succeeded in separating the language groups.