John_Snow_Labs@SMM4H’22: Social Media Mining for Health (#SMM4H) with Spark NLP

Veysel Kocaman, Cabir Celik, Damla Gurbaz, Gursev Pirge, Bunyamin Polat, Halil Saglamlar, Meryem Vildan Sarikaya, Gokhan Turer, David Talby


Abstract
Social media has become a major source of information for healthcare professionals but due to the growing volume of data in unstructured format, analyzing these resources accurately has become a challenge. In this study, we trained health related NER and classification models on different datasets published within the Social Media Mining for Health Applications (#SMM4H 2022) workshop. Transformer based Bert for Token Classification and Bert for Sequence Classification algorithms as well as vanilla NER and text classification algorithms from Spark NLP library were utilized during this study without changing the underlying DL architecture. The trained models are available within a production-grade code base as part of the Spark NLP library; can scale up for training and inference in any Spark cluster; has GPU support and libraries for popular programming languages such as Python, R, Scala and Java.
Anthology ID:
2022.smm4h-1.13
Volume:
Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
SMM4H
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
44–47
Language:
URL:
https://aclanthology.org/2022.smm4h-1.13
DOI:
Bibkey:
Cite (ACL):
Veysel Kocaman, Cabir Celik, Damla Gurbaz, Gursev Pirge, Bunyamin Polat, Halil Saglamlar, Meryem Vildan Sarikaya, Gokhan Turer, and David Talby. 2022. John_Snow_Labs@SMM4H’22: Social Media Mining for Health (#SMM4H) with Spark NLP. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 44–47, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
John_Snow_Labs@SMM4H’22: Social Media Mining for Health (#SMM4H) with Spark NLP (Kocaman et al., SMM4H 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.smm4h-1.13.pdf