  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 long-tail and
homographic emerging entities. An evaluation of relative recall shows that the
method detects 80.4% emerging entities newly registered in Wikipedia; 92.4% of
them are discovered earlier than their registration in Wikipedia, and the
average lead-time is more than one year (571 days).
