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.- Anthology ID:
- 2021.smm4h-1.12
- Volume:
- Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
- Month:
- June
- Year:
- 2021
- Address:
- Mexico City, Mexico
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 74–76
- Language:
- URL:
- https://aclanthology.org/2021.smm4h-1.12
- DOI:
- 10.18653/v1/2021.smm4h-1.12
- Cite (ACL):
- Sergio Santamaría Carrasco and Roberto Cuervo Rosillo. 2021. Word Embeddings, Cosine Similarity and Deep Learning for Identification of Professions & Occupations in Health-related Social Media. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 74–76, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Word Embeddings, Cosine Similarity and Deep Learning for Identification of Professions & Occupations in Health-related Social Media (Santamaría Carrasco & Cuervo Rosillo, SMM4H 2021)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2021.smm4h-1.12.pdf