We make decisions by reacting to changes in the real world, particularly the emergence and disappearance of impermanent entities such as restaurants, services, and events.Because we want to avoid missing out on opportunities or making fruitless actions after those entities have disappeared, it is important to know when * Part of this work was carried out during the period the author was at the University of Tokyo. 1 We release the datasets (tweet IDs) used in the experiments to promote reproducibility.URL I'm so sad to hear that Dave Laing has died.Dave was a very accomplished music industry journalist.Can't believe Google+ is being shut down.It's like when they just pulled Google Friends Connect… Here's your Demolition Day Planner for Martin Tower.A brief, stray shower can't be ruled out… Red Bull Air Race World Championship will not continue after 2019.

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; 92.8% of them are discovered earlier than their registration in Wikipedia, and the average lead-time is more than one year (578 days).

Recently emerged intelligent assistants on smartphones and home electronics (e.g., Siri and Alexa) can be seen as novel hybrids of domain-specific taskoriented spoken dialogue systems and open-domain non-task-oriented ones.To realize such hybrid dialogue systems, this paper investigates determining whether or not a user is going to have a chat with the system.To address the lack of benchmark datasets for this task, we construct a new dataset consisting of 15, 160 utterances collected from the real log data of a commercial intelligent assistant (and will release the dataset to facilitate future research activity).In addition, we investigate using tweets and Web search queries for handling open-domain user utterances, which characterize the task of chat detection.Experiments demonstrated that, while simple supervised methods are effective, the use of the tweets and search queries further improves the F 1 -score from 86.21 to 87.53.