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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5 Hits 1 – 20 of 87 1 Parler comme jamais. La langue, ce qu'on croit et ce qu'on en sait Véron, Laélia; Candea, Maria. - : HAL CCSD, 2021. : Le Robert / Binge audio, 2021 In: https://hal.archives-ouvertes.fr/hal-03377078 ; Le Robert / Binge audio, 2021, 9782321016687 (2021) BASE Show details 2 The Challenges of Digital Diplomacy in the Era of Globalization: The Case of the United Arab Emirates Antwi-Boateng, Osman; Al Mazrouei, Khadija Ali Mohammed In: International Journal of Communication; Vol 15 (2021); 19 ; 1932-8036 (2021) BASE Show details 3 Exploiting BERT and RoBERTa to Improve Performance for Aspect Based Sentiment Analysis Narayanaswamy, Gagan Reddy. - : Technological University Dublin, 2021 In: Dissertations (2021) BASE Show details 4 Distinguishing fake and real news of twitter data with the help of machine learning techniques Shah, Aanan. - : Laurentian University of Sudbury, 2021 BASE Show details 5 Using language models for holistic language variety comparisons ... McNeill, Joshua. - : Open Science Framework, 2021 BASE Show details 6 Orthographic variation of (lol) on Twitter ... McNeill, Joshua. - : Open Science Framework, 2021 BASE Show details 7 Tweeting on dementia: A snapshot of the content and sentiment of tweets associated with dementia Robertshaw, David; Babicova, Ivana In: First Monday; Volume 26, Number 6 - 7 June 2021 ; 1396-0466 (2021) BASE Show details 8 Narrativised simile and emotional responses to Brexit Barbara Dancygier In: Russian Journal of Linguistics, Vol 25, Iss 3, Pp 663-684 (2021) (2021) BASE Show details 9 “You’re too thick to change the station” – Impoliteness, insults and responses to insults on Twitter Bączkowska Anna In: Topics in Linguistics, Vol 22, Iss 2, Pp 62-84 (2021) (2021) BASE Show details 10 Implicit vs explicit evaluation: How English-speaking Twitter users discuss migration problems Elena V. Gabrielova; Olga I. Maksimenko In: Russian Journal of Linguistics, Vol 25, Iss 1, Pp 105-124 (2021) (2021) BASE Show details 11 eterna regularidad de los participios Miguel Silvestre Llamas In: Domínios de Lingu@gem, Vol 16, Iss 2, Pp 843-869 (2021) (2021) BASE Show details 12 Spanish politicians in Twitter: A linguistic analysis of their written discourse Ricardo Casañ Pitarch In: Ibérica, Vol 40, Iss 1, Pp 195-216 (2021) (2021) BASE Show details 13 Construcción de la imagen colectiva de grupos a favor del Acuerdo de paz de Colombia en Twitter / Construction of the collective image of groups in favor of the Colombian peace agreement on Twitter Laura Cristina Bonilla-Neira In: Revista de Estudos da Linguagem, Vol 29, Iss 4, Pp 2225-2257 (2021) (2021) BASE Show details 14 Monitoring Users’ Behavior: Anti-Immigration Speech Detection on Twitter Pitropakis, Nikolaos; Kokot, Kamil; Gkatzia, Dimitra. - : MDPI, 2020 BASE Show details 15 Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19 ... Boukouvalas, Zois; Mallinson, Christine; Crothers, Evan. - : Maryland Shared Open Access Repository, 2020 BASE Show details 16 Temporally-Informed Analysis of Named Entity Recognition ... Rijhwani, Shruti; Preoțiuc-Pietro, Daniel. - : Zenodo, 2020 Abstract: This repository contains the data set developed for the paper: “Shruti Rijhwani and Daniel Preoțiuc-Pietro. Temporally-Informed Analysis of Named Entity Recognition. In Proceedings of the Association for Computational Linguistics (ACL). 2020.” It includes 12,000 tweets annotated for the named entity recognition task. The tweets are uniformly distributed over the years 2014-2019, with 2,000 tweets from each year. The goal is to have a temporally diverse corpus to account for data drift over time when building NER models. The entity types annotated are locations (LOC), persons (PER) and organizations (ORG). The tweets are preprocessed to replace usernames and URLs with a unique token. Hashtags are left intact and can be annotated as named entities. Format The repository contains the annotations in JSON format. Each year-wise file has the tweet IDs along with token-level annotations. The Public Twitter Search API (https://developer.twitter.com/en/docs/tweets/search) can be used extract the text for the tweet ... Keyword: information extraction; named entity recognition; ner; temporal analysis; tweets; twitter; twitter ner URL: https://zenodo.org/record/3899039 https://dx.doi.org/10.5281/zenodo.3899039 BASE Hide details 17 Temporally-Informed Analysis of Named Entity Recognition ... Rijhwani, Shruti; Preoțiuc-Pietro, Daniel. - : Zenodo, 2020 BASE Show details 18 Mapping the languages of Twitter in Finland: richness and diversity in space and time ... Hiippala, Tuomo; Väisänen, Tuomas; Toivonen, Tuuli. - : Zenodo, 2020 BASE Show details 19 Mapping the languages of Twitter in Finland: richness and diversity in space and time ... Hiippala, Tuomo; Väisänen, Tuomas; Toivonen, Tuuli. - : Zenodo, 2020 BASE Show details 20 A large-scale COVID-19 Twitter chatter dataset for open scientific research - an international collaboration ... Banda, Juan M.; Tekumalla, Ramya; Wang, Guanyu. - : Zenodo, 2020 BASE Show details Page: 1 2 3 4 5

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