Abstract
Keeping up to date on emerging entities that appear every day is indispensable for various applications, such as social-trend analysis and marketing research. Previous studies have attempted to
detect unseen entities that are not registered in a
particular knowledge base as emerging entities and
consequently find non-emerging entities since the
absence of entities in knowledge bases does not
guarantee their emergence. We therefore introduce
a novel task of discovering truly emerging entities when they have just been introduced to the
public through microblogs and propose an effective method based on time-sensitive distant supervision, which exploits distinctive early-stage contexts of emerging entities. Experimental results
with a large-scale Twitter archive show that the
proposed method achieves 83.2% precision of the
top 500 discovered emerging entities, which outperforms baselines based on unseen entity recognition with burst detection. Besides notable emerging entities, our method can discover massive longtail and homographic emerging entities. An evaluation of relative recall shows that the method detects 80.4% emerging entities newly registered in
Wikipedia; 92.8% of them are discovered earlier
than their registration in Wikipedia, and the average lead-time is more than one year (578 days).