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2021 NAACL NAACL 2021

Assessing multiple word embeddings for named entity recognition of professions and occupations in health-related social media

Abstract

AbstractThis paper presents our contribution to the ProfNER shared task. Our work focused on evaluating different pre-trained word embedding representations suitable for the task. We further explored combinations of embeddings in order to improve the overall results.

🌉 Interdisciplinary Bridge — Healthcare & Medicine and Interdisciplinary and Machine Learning and Natural Language Processing
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Vasile Pais , Maria Mitrofan

Topics

Machine Learning > Core Methods > Representation Learning Machine Learning > Core Methods > Embedding Learning Healthcare & Medicine > Clinical > Clinical NLP Interdisciplinary > Social > Social Media Analysis Natural Language Processing > Applications > Named Entity Recognition

Keywords

social media analysis named entity recognition pre-trained representation word embedding occupation classification clinical nlp occupation detection
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