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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 - Artificial Intelligence and Healthcare & Medicine and Interdisciplinary and Natural Language Processing

Authors

Vasile Pais, Maria Mitrofan

Topics

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

Keywords

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

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