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1
Characterizing News Portrayal of Civil Unrest in Hong Kong, 1998–2020 ...
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2
Jibes & Delights: A Dataset of Targeted Insults and Compliments to Tackle Online Abuse​ ...
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3
Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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4
Phrase-Level Action Reinforcement Learning for Neural Dialog Response Generation ...
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5
10D: Phonology, Morphology and Word Segmentation #1 ...
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6
Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems ...
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7
19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology - Part 2 ...
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8
18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology - Part 1 ...
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9
The Match-Extend Serialization Algorithm in Multiprecedence ...
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10
Recognizing Reduplicated Forms: Finite-State Buffered Machines ...
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11
Correcting Chinese Spelling Errors with Phonetic Pre-training ...
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12
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction ...
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13
Spatial distributions of languages extracted from Twitter ...
Louf, Thomas. - : figshare, 2021
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14
Spatial distributions of languages on Twitter ...
Louf, Thomas. - : figshare, 2021
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15
Spatial distributions of languages on Twitter ...
Louf, Thomas. - : figshare, 2021
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16
Spatial distributions of languages on Twitter ...
Louf, Thomas. - : figshare, 2021
Abstract: This is a collection of GeoJSON files containing the counts of users of local language groups in every cell of a grid laid over several regions of interest. The cells are defined as squares in a projected system of coordinates adapted to each country, the sides of which have a size X specified in the file names (cell_size=Xm). These counts were obtained through the processing of geo-located tweets posted between 2015 and 2019 in these regions, collected through the streaming API of Twitter, and more specifically using the "statuses/filter" endpoint (see Ref. 1). This endpoint provides a sample of tweets in real time matching some provided filters. Bounding box filters were set to collect tweets from a set of countries of interest. Before reproducing this method of data collection, one should bear in mind that the current form and even the availability of this endpoint is subject to future changes introduced by the Twitter Developer's team. The code used to make this processing as well as to visualize these ...
Keyword: 200402 Computational Linguistics; 200405 Language in Culture and Society Sociolinguistics; 200406 Language in Time and Space incl. Historical Linguistics, Dialectology; 29902 Complex Physical Systems; Computational Physics; FOS Languages and literature; FOS Physical sciences
URL: https://dx.doi.org/10.6084/m9.figshare.14339321.v3
https://figshare.com/articles/dataset/Spatial_distributions_of_languages_extracted_from_Twitter/14339321/3
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17
Including Signed Languages in Natural Language Processing ...
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18
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation ...
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19
The Reading Machine: a Versatile Framework for Studying Incremental Parsing Strategies ...
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20
To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings ...
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