31.112, Confs: Clinical Ling, Comp Ling, Text/Corpus Ling / Spain

[The LINGUIST List]

LINGUIST List: Vol-31-112. Wed Jan 08 2020. ISSN: 1069 - 4875.

Subject: 31.112, Confs: Clinical Ling, Comp Ling, Text/Corpus Ling / Spain

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Date: Wed, 08 Jan 2020 12:12:14
From: Ari Klein [ariklein at pennmedicine.upenn.edu]
Subject: The 5th Social Media Mining for Health Applications Shared Task

 
The 5th Social Media Mining for Health Applications Shared Task 
Short Title: #SMM4H 2020 

Date: 13-Sep-2020 - 13-Sep-2020 
Location: Barcelona, Spain 
Contact: Ari Klein 
Contact Email: ariklein at pennmedicine.upenn.edu 

Linguistic Field(s): Clinical Linguistics; Computational Linguistics; Text/Corpus Linguistics 

Meeting Description: 

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. 
 

Call for Papers:

Paper Submission and Presentation Information:

Participating teams are required to submit a paper describing the system(s)
they ran on the test data. The system description may consist of up to two
pages, plus unlimited references. Sample description systems can be found in
pages 89-136 of the #SMM4H 2019 proceedings. Accepted system descriptions will
be included in the #SMM4H 2020 proceedings. We encourage, but do not require,
at least one author of each accepted system description to register for the
#SMM4H 2020 Workshop, co-located at COLING 2020, and present their system as a
poster. Select participants, as determined by the program committee, will be
invited to extend their system description to up to four pages, plus unlimited
references, and present their system orally. All paper submissions must follow
the COLING guidelines (https://coling2020.org/pages/submission) and be
submitted as a PDF using the Softconf START Conference Manager.

Important Dates

Training data available: January 15, 2020 (may be sooner for some tasks)
Test data available: April 2, 2020
System predictions for test data due: April 5, 2020
System description paper submission deadline: May 5, 2020
Notification of acceptance of system description papers: June 10, 2020
Camera-ready papers due: June 30, 2020
Workshop: September 13, 2020





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