Named Entity Recognition (NER) is a key component in NLP systems for question
answering, information retrieval, relation extraction, etc. NER systems have been studied …

A vast amount of location information exists in unstructured texts, such as social media
posts, news stories, scientific articles, web pages, travel blogs, and historical archives …

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 study the open-domain named entity recognition (NER) problem under distant
supervision. The distant supervision, though does not require large amounts of manual …

In a surprising turn, Large Language Models (LLMs) together with a growing arsenal of
prompt-based heuristics now offer powerful off-the-shelf approaches providing few-shot …

Numerous important events happen everyday and everywhere but are reported in different
media sources with different narrative styles. How to detect whether real-world events have …

In this paper, we study Multimodal Named Entity Recognition (MNER) for social media posts.
Existing approaches for MNER mainly suffer from two drawbacks:(1) despite generating …

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 …

Contextualized word embeddings such as ELMo and BERT provide a foundation for strong
performance across a wide range of natural language processing tasks by pretraining on …

Abstract In recent years, Multimodal Named Entity Recognition (MNER) on social media has
attracted considerable attention. However, existing MNER studies only extract entity-type …