Nov 19, 2020 -- WNUT workshop is going virtual together with EMNLP 2020
The WNUT workshop focuses on Natural Language Processing applied to noisy user-generated text, such as that found in social media, online reviews, crowdsourced data, web forums, clinical records and language learner essays. The workshop hashtag is #wnut.
We are organizing three shared-tasks:
(1) Entity and relation recognition over wet-lab protocols. Data is released on June 08, 2020! Official evaluation will be August 31 ~ September 04, 2020.
(2) Identification of informative COVID-19 English Tweets. Data is released on June 21, 2020! Official evaluation will be August 17 ~ 21, 2020.
We have best paper awards sponsored by Twitter this year.
We seek submissions of
long and short papers on original and unpublished work (same page limit EMNLP main conference). All accepted submissions will be presented as pre-recorded talks at the workshop, following the EMNLP 2020 main conference (more details here).
Topics of interest include but are not limited to:
Lab protocols specify steps in performing a lab procedure. They are noisy, dense, and domain-specific. Automatic or semi-automatic conversion of protocols into machine-readable format benefits biological research. In this task, system entries are invited for event recognition and relation extraction over these lab protocols. Note that these protocols are written by researchers and lab technicians worldwide, some of which may contain non-standard language or spelling errors. Here's a sample of the input data:
Initial data is released on June 8, 2020. Please register here to receive future data for the official evaluation (Aug 31 - Sep 4, 2020).
The goals of this shared task are: (1) To develop a language processing task that potentially impacts research and downstream applications, and (2) To provide the community with a new dataset for identifying informative COVID-19 English Tweets.
For this task, participants are asked to develop systems that automatically identify whether an English Tweet related to the novel coronavirus (COVID-19) is informative or not. Such informative Tweets provide information about recovered, suspected, confirmed and death cases as well as location or travel history of the cases. The dataset and systems developed for this shared task will be beneficial for the development of COVID-19 related monitoring systems.
People usually share a wide variety of information related to COVID-19 publicly on social media. For example, Twitter users often indicate when they might be at increased risk of COVID-19 due to a coworker or other close contact testing positive for the virus, or when they have symptoms but were denied access to testing. In this shared task, participants are invited to develop systems that automatically extract COVID-19 related events from Twitter using our newly built corpus. Here is an example of our annotated data:
Initial data has been released on June 22, 2020. Please register here to receive future data for the official evaluation (Sep 7 - Sep 11, 2020).