We make decisions by reacting to changes in the real world, in particular,
the emergence and disappearance of impermanent entities such as events,
restaurants, and services. Because we want to avoid missing out on
opportunities or making fruitless actions after they have disappeared, it is
important to know when entities disappear as early as possible. We thus tackle
the task of detecting disappearing entities from microblogs, whose posts
mention various entities, in a timely manner. The major challenge is detecting
uncertain contexts of disappearing entities from noisy microblog posts. To
collect these disappearing contexts, we design time-sensitive distant
supervision, which utilizes entities from the knowledge base and time-series
posts, for this task to build large-scale Twitter datasets\footnote{We will
release the datasets (tweet IDs) used in the experiments to promote
reproducibility.} for English and Japanese. To ensure robust detection in noisy
environments, we refine pretrained word embeddings of the detection model on
microblog streams of the target day. Experimental results on the Twitter
datasets confirmed the effectiveness of the collected labeled data and refined
word embeddings; more than 70\% of the detected disappearing entities in
Wikipedia are discovered earlier than the update on Wikipedia, and the average
lead-time is over one month.

通过微博的时序帖子，使用具有时间敏感性的远程监督来检测消失的实体，经实验验证使用收集的标记数据和改进的词嵌入的方法能够在嘈杂的环境中获得 70％以上的准确率并且能提前一个月以上发现消失的实体。

微博中消失实体的早期发现

Early Discovery of Disappearing Entities in Microblogs

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).

本研究提出了一种基于时态远程监督的有效方法，以揭示刚被引入公众视野通过微博出现的真正的新兴实体，并以大规模 Twitter 存档的实验结果证明，该方法发现的前 500 个新兴实体的精度达到了 83.2％。