Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Segun Taofeek Aroyehun"'
Autor:
Segun Taofeek Aroyehun, Lukas Malik, Hannah Metzler, Nikolas Haimerl, Anna Di Natale, David Garcia
Publikováno v:
EPJ Data Science, Vol 12, Iss 1, Pp 1-21 (2023)
Abstract The wealth of text data generated by social media has enabled new kinds of analysis of emotions with language models. These models are often trained on small and costly datasets of text annotations produced by readers who guess the emotions
Externí odkaz:
https://doaj.org/article/2c0c9754e01746b0a6b10b3afc491faf
Publikováno v:
Neurocomputing. 464:421-431
When labels are organized into a meaningful taxonomy, the parent-child relationship between labels at different levels can give the classifier additional information not deducible from the data alone, especially with limited training data. As a case
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::86687e9c78456c4c44765ebdad9734c2
https://doi.org/10.1016/j.neucom.2021.07.057
https://doi.org/10.1016/j.neucom.2021.07.057
Autor:
Jana Lasser, Segun Taofeek Aroyehun, Almog Simchon, Fabio Carrella, David Garcia, Stephan Lewandowsky
Publikováno v:
Lasser, J, Aroyehun, S T, Simchon, A, Carrella, F, Garcia, D & Lewandowsky, S 2022, ' Social media sharing of low quality news sources by political elites ', PNAS Nexus, vol. 1, no. 4, pgac186 . https://doi.org/10.1093/pnasnexus/pgac186
PNAS Nexus
PNAS Nexus
Increased sharing of untrustworthy information on social media platforms is one of the main challenges of our modern information society. Because information disseminated by political elites is known to shape citizen and media discourse, it is partic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::818b5a713ac38a71d110a93fd3b83b5e
https://research-information.bris.ac.uk/en/publications/d45a86f7-38ca-4390-9067-43ba462f90b5
https://research-information.bris.ac.uk/en/publications/d45a86f7-38ca-4390-9067-43ba462f90b5
We present our systems and findings for the prerequisite relation learning task (PRELEARN) at EVALITA 2020. The task aims to classify whether a pair of concepts hold a prerequisite relation or not. We model the problem using handcrafted features and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ea4f964912f30497463967045acc65a2
https://doi.org/10.4000/books.aaccademia.7565
https://doi.org/10.4000/books.aaccademia.7565
Publikováno v:
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task.
This paper details our approach to the task of detecting reportage of adverse drug reaction in tweets as part of the 2019 social media mining for healthcare applications shared task. We employed a combination of three types of word representations as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1ecda5aae8a738946757ece260e41ea4
https://doi.org/10.18653/v1/w19-3224
https://doi.org/10.18653/v1/w19-3224
Publikováno v:
BEA@NAACL-HLT
We describe the systems of NLP-CIC team that participated in the Complex Word Identification (CWI) 2018 shared task. The shared task aimed to benchmark approaches for identifying complex words in English and other languages from the perspective of no
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a65ceabce953097886d6b37246cdc31e
https://doi.org/10.18653/v1/w18-0538
https://doi.org/10.18653/v1/w18-0538
Publikováno v:
Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task.
We describe our submissions to the Third Social Media Mining for Health Applications Shared Task. We participated in two tasks (tasks 1 and 3). For both tasks, we experimented with a traditional machine learning model (Naive Bayes Support Vector Mach
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a3ba8fb96076bbb2796831c041407823
https://doi.org/10.18653/v1/w18-5915
https://doi.org/10.18653/v1/w18-5915