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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.
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).
Physicists look at language
(2006)
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.
Based on specific linguistic landmarks in the speech signal, this study investigates pitch level and pitch span differences in English, German, Bulgarian and Polish. The analysis is based on 22 speakers per language (11 males and 11 females). Linear mixed models were computed that include various linguistic measures of pitch level and span, revealing characteristic differences across languages and between language groups. Pitch level appeared to have significantly higher values for the female speakers in the Slavic than the Germanic group. The male speakers showed slightly different results, with only the Polish speakers displaying significantly higher mean values for pitch level than the German males. Overall, the results show that the Slavic speakers tend to have a wider pitch span than the German speakers. But for the linguistic measure, namely for span between the initial peaks and the non-prominent valleys, we only find the difference between Polish and German speakers. We found a flatter intonation contour in German than in Polish, Bulgarian and English male and female speakers and differences in the frequency of the landmarks between languages. Concerning “speaker liveliness” we found that the speakers from the Slavic group are significantly livelier than the speakers from the Germanic group.
New KARL (Knowledge Acquisition and Representation Language) allows to specify all parts of a problem-solving method (PSM). It is a formal language with a well-defined semantics and thus allows to represent PSMs precisely and unambiguously yet abstracting from implementation detail. In this paper it is shown how the language KARL has been modified and extended to New KARL to better meet the needs for the representation of PSMs. Based on a conceptual structure of PSMs new language primitives are introduced for KARL to specify such a conceptual structure and to support the configuration of methods. An important goal for this extension was to preserve three important properties of KARL: to be (i) a conceptual, (ii) a formal, and (iii) an executable language.
In the first part of this contribution, we will present, as a starting point for the following discussions, a simple formal language P containing one stative predicate. We will then discuss, on an intuitive level, how a treatment of predicates of change could be conceived, and how the progressive could be rendered in a formal language.
We will then give a formal definition of a language, TP1, based on P, and we will construct a semantics for TP1, which incorporates the ideas discussed.
While adjusting to the COVID-19 pandemic, people around the world started to talk about the “new normal” way of life, and they conveyed feelings and thoughts on the topic through social networks and traditional communication channels resorting to a set of specific linguistic strategies, such as metaphors and neologisms. The vocabulary in different domains and in everyday speech was expanded to accommodate a complex social, cultural, and professional phenomenon of changes. Therefore, this new life gave birth to a new language – the “coronaspeak”. According to Thorne (2020), the “coronaspeak” has three stages: first, it emerged in the way medical aspects were communicated in everyday language; secondly, it occurred when speakers verbalized the experiences they had undergone and “invented their own terms”; finally, this “new” way of speaking emerged in the government and authorities’ jargon, to ensure that the new rules and policies were understood, and that population adopted socially responsible behaviours.
In this paper, we will focus on the second stage, because we intend to take stock of how speakers communicate and verbalize this new way of living, particularly on social networks, for example. Alongside, we are interested in the context in which the neologism – be it a new word, a new meaning, or a new use – emerged, is used, and understood, through the observation of the occurrence of the new word(s) either on social networks or through dissemination texts (press) to confront it with the ones that Portuguese digital dictionaries have attested so far. Different criteria regarding the insertion of new units, the inclusion date, and the lexicographic description of the entries in the dictionaries will be debated.
In this paper, we present first results of training a classifier for discriminating Russian texts into different levels of difficulty. For the classification we considered both surface-oriented features adopted from readability assessments and more linguistically informed, positional features to classify texts into two levels of difficulty. This text classification is the main focus of our Levelled Study Corpus of Russian (LeStCoR), in which we aim to build a corpus adapted for language learning purposes – selecting simpler texts for beginner second language learners and more complex texts for advanced learners. The most discriminative feature in our pilot study was a lexical feature that approximates accessibility of the vocabulary by the second language learner in terms of the proportion of familiar words in the texts. The best feature setting achieved an accuracy of 0.91 on a pilot corpus of 209 texts.