@inproceedings{mesa-murgado-etal-2021-identifying,
title = "Identifying professions {\&} occupations in Health-related Social Media using Natural Language Processing",
author = "Mesa Murgado, Alberto and
Parras Portillo, Ana and
L{\'o}pez {\'U}beda, Pilar and
Martin, Maite and
Ure{\~n}a-L{\'o}pez, Alfonso",
editor = "Magge, Arjun and
Klein, Ari and
Miranda-Escalada, Antonio and
Al-garadi, Mohammed Ali and
Alimova, Ilseyar and
Miftahutdinov, Zulfat and
Farre-Maduell, Eulalia and
Lopez, Salvador Lima and
Flores, Ivan and
O'Connor, Karen and
Weissenbacher, Davy and
Tutubalina, Elena and
Sarker, Abeed and
Banda, Juan M and
Krallinger, Martin and
Gonzalez-Hernandez, Graciela",
booktitle = "Proceedings of the Sixth Social Media Mining for Health ({\#}SMM4H) Workshop and Shared Task",
month = jun,
year = "2021",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.smm4h-1.31/",
doi = "10.18653/v1/2021.smm4h-1.31",
pages = "141--145",
abstract = "This paper describes the entry of the research group SINAI at SMM4H`s ProfNER task on the identification of professions and occupations in social media related with health. Specifically we have participated in Task 7a: Tweet Binary Classification to determine whether a tweet contains mentions of occupations or not, as well as in Task 7b: NER Offset Detection and Classification aimed at predicting occupations mentions and classify them discriminating by professions and working statuses."
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<abstract>This paper describes the entry of the research group SINAI at SMM4H‘s ProfNER task on the identification of professions and occupations in social media related with health. Specifically we have participated in Task 7a: Tweet Binary Classification to determine whether a tweet contains mentions of occupations or not, as well as in Task 7b: NER Offset Detection and Classification aimed at predicting occupations mentions and classify them discriminating by professions and working statuses.</abstract>
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%0 Conference Proceedings
%T Identifying professions & occupations in Health-related Social Media using Natural Language Processing
%A Mesa Murgado, Alberto
%A Parras Portillo, Ana
%A López Úbeda, Pilar
%A Martin, Maite
%A Ureña-López, Alfonso
%Y Magge, Arjun
%Y Klein, Ari
%Y Miranda-Escalada, Antonio
%Y Al-garadi, Mohammed Ali
%Y Alimova, Ilseyar
%Y Miftahutdinov, Zulfat
%Y Farre-Maduell, Eulalia
%Y Lopez, Salvador Lima
%Y Flores, Ivan
%Y O’Connor, Karen
%Y Weissenbacher, Davy
%Y Tutubalina, Elena
%Y Sarker, Abeed
%Y Banda, Juan M.
%Y Krallinger, Martin
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
%D 2021
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F mesa-murgado-etal-2021-identifying
%X This paper describes the entry of the research group SINAI at SMM4H‘s ProfNER task on the identification of professions and occupations in social media related with health. Specifically we have participated in Task 7a: Tweet Binary Classification to determine whether a tweet contains mentions of occupations or not, as well as in Task 7b: NER Offset Detection and Classification aimed at predicting occupations mentions and classify them discriminating by professions and working statuses.
%R 10.18653/v1/2021.smm4h-1.31
%U https://aclanthology.org/2021.smm4h-1.31/
%U https://doi.org/10.18653/v1/2021.smm4h-1.31
%P 141-145
Markdown (Informal)
[Identifying professions & occupations in Health-related Social Media using Natural Language Processing](https://aclanthology.org/2021.smm4h-1.31/) (Mesa Murgado et al., SMM4H 2021)
ACL