Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Zach Wood-Doughty"'
Autor:
Amir Feder, Katherine A. Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E. Roberts, Brandon M. Stewart, Victor Veitch, Diyi Yang
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 10, Pp 1138-1158 (2022)
AbstractA fundamental goal of scientific research is to learn about causal relationships. However, despite its critical role in the life and social sciences, causality has not had the same importance in Natural Language Processing (NLP), which has tr
Externí odkaz:
https://doaj.org/article/ce20dd42414b496493ce8c27322a7d13
Autor:
Eli Sherman, Diane Alejo, Zach Wood-Doughty, Marc Sussman, Stefano Schena, Chin Siang Ong, Eric Etchill, Joe DiNatale, Narges Ahmidi, Ilya Shpitser, Glenn Whitman
Publikováno v:
The Annals of Thoracic Surgery. 114:2173-2179
Hospital readmission within 30 days of discharge is a well-studied outcome. Predicting readmission after cardiac surgery, however, is notoriously challenging; the best-performing models in the literature have areas under the curve around .65. A relia
Publikováno v:
Proceedings of the 21st Workshop on Biomedical Language Processing.
Publikováno v:
2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA).
Autor:
Amir Feder, Katherine A. Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E. Roberts, Brandon M. Stewart, Victor Veitch, Diyi Yang
A fundamental goal of scientific research is to learn about causal relationships. However, despite its critical role in the life and social sciences, causality has not had the same importance in Natural Language Processing (NLP), which has traditiona
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f02bbc451da99921782b108109d425f3
http://arxiv.org/abs/2109.00725
http://arxiv.org/abs/2109.00725
Publikováno v:
SocialNLP@NAACL
Computational social science studies often contextualize content analysis within standard demographics. Since demographics are unavailable on many social media platforms (e.g. Twitter) numerous studies have inferred demographics automatically. Despit
Publikováno v:
Proc ACM Hum Comput Interact
The #MeToo movement on Twitter has drawn attention to the pervasive nature of sexual harassment and violence. While #MeToo has been praised for providing support for self-disclosures of harassment or violence and shifting societal response, it has al
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6a02780cf2bd52e131548c0c966c3206
Autor:
DureAden Khan, David A. Broniatowski, Mark Dredze, Sandra Crouse Quinn, Amelia M. Jamison, Zach Wood-Doughty
Publikováno v:
Vaccine. 38(3)
Background In 2018, Facebook introduced Ad Archive as a platform to improve transparency in advertisements related to politics and “issues of national importance.” Vaccine-related Facebook advertising is publicly available for the first time. Aft
Publikováno v:
NUT@EMNLP
While recurrent neural networks (RNNs) are widely used for text classification, they demonstrate poor performance and slow convergence when trained on long sequences. When text is modeled as characters instead of words, the longer sequences make RNNs
Publikováno v:
PEOPLES@NAACL-HTL
Twitter user accounts include a range of different user types. While many individuals use Twitter, organizations also have Twitter accounts. Identifying opinions and trends from Twitter requires the accurate differentiation of these two groups. Previ