@inproceedings{santamaria-carrasco-cuervo-rosillo-2021-word,
title = "Word Embeddings, Cosine Similarity and Deep Learning for Identification of Professions {\&} Occupations in Health-related Social Media",
author = "Santamar{\'\i}a Carrasco, Sergio and
Cuervo Rosillo, Roberto",
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-1.12",
doi = "10.18653/v1/2021.smm4h-1.12",
pages = "74--76",
abstract = "ProfNER-ST focuses on the recognition of professions and occupations from Twitter using Spanish data. Our participation is based on a combination of word-level embeddings, including pre-trained Spanish BERT, as well as cosine similarity computed over a subset of entities that serve as input for an encoder-decoder architecture with attention mechanism. Finally, our best score achieved an F1-measure of 0.823 in the official test set.",
}
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%0 Conference Proceedings
%T Word Embeddings, Cosine Similarity and Deep Learning for Identification of Professions & Occupations in Health-related Social Media
%A Santamaría Carrasco, Sergio
%A Cuervo Rosillo, Roberto
%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 santamaria-carrasco-cuervo-rosillo-2021-word
%X ProfNER-ST focuses on the recognition of professions and occupations from Twitter using Spanish data. Our participation is based on a combination of word-level embeddings, including pre-trained Spanish BERT, as well as cosine similarity computed over a subset of entities that serve as input for an encoder-decoder architecture with attention mechanism. Finally, our best score achieved an F1-measure of 0.823 in the official test set.
%R 10.18653/v1/2021.smm4h-1.12
%U https://aclanthology.org/2021.smm4h-1.12
%U https://doi.org/10.18653/v1/2021.smm4h-1.12
%P 74-76
Markdown (Informal)
[Word Embeddings, Cosine Similarity and Deep Learning for Identification of Professions & Occupations in Health-related Social Media](https://aclanthology.org/2021.smm4h-1.12) (Santamaría Carrasco & Cuervo Rosillo, SMM4H 2021)
ACL