Overview of the Fourth Social Media Mining for Health (SMM4H) Shared Tasks at ACL 2019

no code implementations • WS 2019 • Davy Weissenbacher, Abeed Sarker, Arjun Magge, Ashlynn Daughton, Karen O{'}Connor, Michael J. Paul, Gonzalez-Hern, Graciela ez

We present the Social Media Mining for Health Shared Tasks collocated with the ACL at Florence in 2019, which address these challenges for health monitoring and surveillance, utilizing state of the art techniques for processing noisy, real-world, and substantially creative language expressions from social media users.

Task 2

SemEval-2019 Task 12: Toponym Resolution in Scientific Papers

no code implementations • SEMEVAL 2019 • Davy Weissenbacher, Arjun Magge, Karen O{'}Connor, Matthew Scotch, Gonzalez-Hern, Graciela ez

We also analyze the methods, the results and the errors made by the competing systems with a focus on toponym disambiguation.

Toponym Resolution

Overview of the Third Social Media Mining for Health (SMM4H) Shared Tasks at EMNLP 2018

no code implementations • WS 2018 • Davy Weissenbacher, Abeed Sarker, Michael J. Paul, Gonzalez-Hern, Graciela ez

The goals of the SMM4H shared tasks are to release annotated social media based health related datasets to the research community, and to compare the performances of natural language processing and machine learning systems on tasks involving these datasets.

General Classification Task 2 +1

Dealing with Medication Non-Adherence Expressions in Twitter

no code implementations • WS 2018 • Takeshi Onishi, Davy Weissenbacher, Ari Klein, Karen O{'}Connor, Gonzalez-Hern, Graciela ez

Through a semi-automatic analysis of tweets, we show that Twitter users not only express Medication Non-Adherence (MNA) in social media but also their reasons for not complying; further research is necessary to fully extract automatically and analyze this information, in order to facilitate the use of this data in epidemiological studies.