Named entity recognition (NER) is the task to identify mentions of rigid designators from text
belonging to predefined semantic types such as person, location, organization etc. NER …

Biomedical-named entity recognition (bNER) is critical in biomedical informatics. It identifies
biomedical entities with special meanings, such as people, places, and organizations, as …

We present BERTweet, the first public large-scale pre-trained language model for English
Tweets. Our BERTweet, having the same architecture as BERT-base (Devlin et al., 2019), is …

We present MultiCoNER, a large multilingual dataset for Named Entity Recognition that
covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as …

We present the findings of SemEval-2022 Task 11 on Multilingual Complex Named Entity
Recognition MULTICONER. Divided into 13 tracks, the task focused on methods to identify …

This shared task focuses on identifying unusual, previously-unseen entities in the context of
emerging discussions. Named entities form the basis of many modern approaches to other …

Automatic resolution of rumours is a challenging task that can be broken down into smaller
components that make up a pipeline, including rumour detection, rumour tracking and …

Everyday billions of multimodal posts containing both images and text are shared in social
media sites such as Snapchat, Twitter or Instagram. This combination of image and text in a …

In this study, we propose a new multi-task learning approach for rumor detection and stance
classification tasks. This neural network model has a shared layer and two task specific …

We introduce a new task called Multimodal Named Entity Recognition (MNER) for noisy user-
generated data such as tweets or Snapchat captions, which comprise short text with …