This study presents the outcomes of the shared task competition BioCreative VII (Task 3)
focusing on the extraction of medication names from a Twitter user's publicly available …

In this paper, we present our work participating in the BioCreative VII Track 3-automatic
extraction of medication names in tweets, where we implemented a multi-task learning …

To process natural language and to extract information from text, transformers are currently
the model of choice for many different tasks. Conversely, if the number of training examples …

We present the BioCreative VII Task 3 which focuses on drug names extraction from tweets.
Recognized to provide unique insights into population health, detecting health related …

Automatically extracting medication names from tweets is challenging in the real world.
There are many tweets; however, only a small proportion mentions medications. Thus …

BioCreative VII Track 3-Automatic extraction of medication names in tweets. Automatically
extracting medication names from imbalanced data is challenging for deep learning models …

We present a modular model that leverages knowledge sources including specialized
gazetteer lists, morphological information, and contextualized language models for the task …

A large volume of layperson-authored messages get posted and consumed on Twitter,
which makes it an important source for public health-related studies. The Task 3 of the …

We sought to develop an automated approach for classifying social media posts (" tweets")
by 212 pregnant users. Part of the BioCreative VII-Track 3 challenge, we sought to …

In an era where medicine and technology are closely intertwined, sources of patient-
generated data such as social media content are being explored to extract important …