#SMM4H 2020 : Deadlines extended for the 5th Social Media Mining for Health Applications Shared Task
Call For Papers
Following the postponement of COLING 2020 (https://coling2020.org/) due to the coronavirus outbreak, we have extended the #SMM4H 2020 Shared Task deadlines. We are optimistic that the postponement will allow presentations for #SMM4H 2020 to be in person in Barcelona, but we will accommodate virtual oral presentations of accepted papers by authors who are unable to travel due to the impact of coronavirus.
The Social Media Mining for Health Applications (#SMM4H) Shared Task involves natural language processing (NLP) challenges of using social media data for health research, including informal, colloquial expressions and misspellings of clinical concepts, noise, data sparsity, ambiguity, and multilingual posts. For each of the five tasks below, participating teams will be provided with a set of annotated tweets for developing systems, followed by a three-day window during which they will run their systems on unlabeled test data. For additional details about the tasks and information about registration, data access, paper submissions, and presentations, go to https://healthlanguageprocessing.org/smm4h-sharedtask-2020/.
Task 1: Automatic classification of tweets that mention medications
This binary classification task involves distinguishing tweets that mention a medication or dietary supplement from those that do not.
Task 2: Automatic classification of multilingual tweets that report adverse effects
This binary classification task involves distinguishing tweets that report an adverse effect (AE) of a medication from those that do not, taking into account subtle linguistic variations between AEs and indications (i.e., the reason for using the medication). This task includes distinct sets of tweets posted in English, Spanish, French, and Russian.
Task 3: Automatic extraction and normalization of adverse effects in English tweets
This task is an end-to-end task that involves extracting the span of text containing an adverse effect (AE) of a medication from tweets that report an AE, and then mapping the extracted AE to a standard concept ID in the MedDRA vocabulary (preferred terms).
Task 4: Automatic characterization of chatter related to prescription medication abuse in tweets
This multi-class classification task involves distinguishing, among tweets that mention at least one prescription opioid, benzodiazepine, atypical anti-psychotic, central nervous system stimulant or GABA analogue, tweets that report potential abuse/misuse from those that report non-abuse/-misuse consumption, merely mention the medication, or are unrelated.
Task 5: Automatic classification of tweets reporting a birth defect pregnancy outcome
This multi-class classification task involves distinguishing three classes of tweets that mention birth defects: “defect” tweets refer to the user’s child and indicate that he/she has the birth defect mentioned in the tweet; “possible defect” tweets are ambiguous about whether someone is the user’s child and/or has the birth defect mentioned in the tweet; “non-defect” tweets merely mention birth defects.
Test data available: June 1, 2020
System predictions for test data due: June 4, 2020
System description paper submission deadline: July 1, 2020
Notification of acceptance of system description papers: September 1, 2020
Camera-ready papers due: October 1, 2020
Workshop: December 12 or 13, 2020 (TBA)
Graciela Gonzalez-Hernandez, University of Pennsylvania, USA
Davy Weissenbacher, University of Pennsylvania, USA
Ari Z. Klein, University of Pennsylvania, USA
Karen O’Connor, University of Pennsylvania, USA
Ivan Flores, University of Pennsylvania
Abeed Sarker, Emory University, USA
Arjun Magge, Arizona State University, USA
Elena Tutubalina, Kazan Federal University, Russia
Zulfat Miftahutdinov, Kazan Federal University, Russia
Ilsear Alimova, Kazan Federal University, Russia
Martin Krallinger, Barcelona Supercomputing Center, Spain
Anne-Lyse Minard, Université d’Orléans, France
Olivier Bodenreider, US National Library of Medicine, USA
Kevin Cohen, University of Colorado School of Medicine, USA
Robert Leaman, US National Library of Medicine, USA
Diego Molla, Macquarie University, Australia
Zhiyong Lu, US National Library of Medicine, USA
Azadeh Nikfarjam, Apple, USA
Thierry Poibeau, French National Center for Scientific Research, France
Kirk Roberts, University of Texas Health Science Center at Houston, USA
Yutaka Sasaki, Toyota Technological Institute, Japan
H. Andrew Schwartz, Stony Brook University, USA
Nicolas Turenne, French National Institute for Agricultural Research, France
Karin Verspoor, University of Melbourne, Australia
Pierre Zweigenbaum, French National Center for Scientific Research, France
Ari Klein (email@example.com)