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475 results for “words”
THE HISTORY OF "WISE WORDS" IN LINGUISTICS
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TRANSLATION OF NATIONAL WORDS IN ARTISTIC WORKS TRANSLATION
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SOME CHARACTERISTICS OF WORD SEMANTIC SPECIFICATIONS IN FRENCH AND UZBEKI LANGUAGES
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THE THEORY OF THE ORIGIN OF ONOMATOPOEIC WORDS.
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Diachronic semantics: changes of meaning of words over time and the consequences for keeping classification systems up to date
<p>Meanings of words in a natural language are changing over time under the influences of different factors. Words adapt to new meanings, lose old meanings, rearrange current meanings and change some parts of previous meanings, etc. The language as a living organism needs to be able to accept and adapt to those changes. Like natural languages, artificial languages such as classification systems or subject indexing systems have to adjust to those changes too. Linguist F. de Saussure defines language as a 'system of signs'. If we transfer this definition into the artificial language such as a classification system (e.g., Universal Decimal Classification (UDC)) and define it also as a 'system of signs' which has a vocabulary, a grammar and a syntax, we could draw parallels between phenomena which occur in natural and in artificial languages. The young linguistic discipline which deals with changes in meanings over time is called diachronic semantics and this paper explores how its mechanisms can be used to analyze the changes that occur in classification systems over time. Diachronic semantics uses various mechanisms to describe adapting and changing meanings of words. These mechanisms include: metaphor, metonymy, specialization, generalization, analogy and splitting. This paper also aims to explain the borrowing and adjusting of the theory from the field of linguistics into the field of information sciences.</p>
SEMANTIC STRUCTURE OF THE WORD IN WORLD LINGUISTICS
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THE DENOTATIVE SYSTEMATIZATION OF SOME WORDS OF ARABIC ORIGIN IN THE TURKIC WORK "KISASI RABGUZI"
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WAYS OF FORMING OF COMPOUND WORDS IN RUSSIAN AND ENGLISH LANGUAGES.
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ICDAR19 words
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IAM_words
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CVL words
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Firemaker_words
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COGNITIVE CHARACTERISTICS OF WORD ORDER IN ADVERTISING TEXTS
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EQUIVALENT WORDS AND THEIR REFLECTION IN A WORK OF ART
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STRUCTURAL FEATURES OF HURUF (AUXILIARY WORDS) IN THE WORK OF «MUQADDAMATU- L-ADAB»
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THE THEORY OF THE VERB WORD CLASS IN MODERN ENGLISH
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GMW-Greek Misspelled Words
<p>This dataset contains 574,883 distinct Greek words and for each one of them it contains various misspellings, in average 4.32 misspellings per word. In total, it contains more than 3 million forms of Greek words (3,063,143). The misspelled words have been produced algorithmically. The dataset can be used for evaluating methods for approximate matching, error correction, etc.</p>
Study of terminological subsystems of modern school textbooks in Russian with the help of word embedding models Word2Vec and neural networks
<p>The reported study was funded by RFBR, project number 19-29-14032 mk.</p>
Supplementary material 1 from: Golebie EJ, van Riper CJ, Arlinghaus R, Gaddy M, Jang S, Kochalski S, Lu Y, Olden JD, Stedman R, Suski C (2022) Words matter: a systematic review of communication in non-native aquatic species literature. NeoBiota 74: 1-28. https://doi.org/10.3897/neobiota.74.79942
Codebook
Frequency of positive words in grant applications
<p>The data was gathered to reproduce the methodology and findings presented in <a href="http://dx.doi.org/10.1136/bmj.l6573">Lerchenmueller et al. (2019)</a> in proposal texts that were submitted to different funding schemes offered by the Swiss National Science Foundation.</p> <h2>Data description</h2> <p>The data files (as .xlsx) contains three sheets:</p> <ol> <li>Career funding schemes (excluding fellowships): 1802 proposals included</li> <li>Spark funding scheme: 612 proposals included</li> <li>Project funding scheme (Projects): 5736 proposals included</li> </ol> <p>Each Sheet includes a data matrix where each row is a specific grant proposal. The unit of analysis are grant proposals and the texts used are the title and abstracts. The data is used in a project available from github (https://github.com/snsf-data/positive_language). [Paper soon to be submitted]</p> <p>The first 25 columns give us the 25 positive words and their respective counts in each of the analysed texts of the grant proposals. The <strong>positiv words </strong>(here the column names are) used are the following: amazing, assuring, astonishing, bright, creative, encouraging, enormous, excellent, favorable/ favourable, groundbreaking, hopeful, innovative, inspiring, inventive, novel, phenomenal, prominent, promising, reassuring, remarkable, robust, spectacular, supportive, unique, and unprecedented. Those were first proposed by <a href="https://www.bmj.com/content/351/bmj.h6467">Vinkers et al. (2015)</a>. </p> <p>Additionnally, the following columns are present in the data:</p> <ul> <li>sum_pos: The sum of the number of positive words in the texts. </li> <li>text_length and text_length100: the text length (count of words), and text length divided by 100.</li> <li>ResponsibleApplicantGender: the gender of the corresponding applicant (m or f)</li> <li>ResponsibleApplicantAge: the age of the corresponding applicant at submission (continuous)</li> <li>NationalityIsoCode: the nationality of the corresponding applicant (CH or not CH)</li> <li>IsApproved and IsFundable: binary variable indicating funding success, or whether the project would have been fundable given the grade with unlimited funding. </li> <li>Decision and CallYear: year of the call deadline, and year the funding decision was taken. </li> <li>ResearchInstitutionType: the type of institution the corresponding applicant is affiliated to (Cantonal University, ETH Domain, Other)</li> <li>which_lang: the language the proposal was written in (all english)</li> </ul> <h2>Text processing</h2> <p>The text corpus used in this analysis was also used as the basis for additional analyses. It therefore underwent a thorough cleaning with the help of the R-packages {tm} and {stringr}. After the pre-processing steps, the number of times each of the postitive words (see above) occured in the title and abstracts of the respective proposal is computed using a simple keyword search. </p> <h3>Pre-processing steps: </h3> <ul> <li>Punctuation was removed.</li> <li>All non-standard alphanumeric characters were removed. </li> <li>All characters were converted to lowercase. </li> <li>Extra white spaces were removed. </li> <li>Internet formatting was removed: URLs, email addresses, twitter formatting (words starting with # and @). </li> <li>Common English contractions were converted to their non-contracted form ("it's" --> "it is"). </li> <li>English language stopwords were removed. </li> </ul> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.