Early Discovery of Disappearing Entities in Microblogs (2023)

Abstract: 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 entities disappear as early as possible. We thus tackle the task of detecting disappearing entities from microblogs where various information is shared timely. The major challenge is detecting uncertain contexts of disappearing entities from noisy microblog posts. To collect such disappearing contexts, we design time-sensitive distant supervision, which utilizes entities from the knowledge base and time-series posts. Using this method, we actually build large-scale Twitter datasets of disappearing entities. To ensure robust detection in noisy environments, we refine pretrained word embeddings for the detection model on microblog streams in a timely manner. Experimental results on the Twitter datasets confirmed the effectiveness of the collected labeled data and refined word embeddings; the proposed method outperformed a baseline in terms of accuracy, and more than 70% of the detected disappearing entities in Wikipedia are discovered earlier than the update on Wikipedia, with the average lead-time is over one month.

## introduction our daily actions depend on the state of the real world and its changes, especially changes in the entities available to us. among the various changes, the beginning of entities, or emerging entities @xcite such as new songs, movies, and events, are useful for understanding 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… pilot lineup for 2018 red bull air race world championship: britain is back! url happy new year everyone! i hope this year will be better for all of us and pristin will achieve a lot! figure 1: tweets of entities with non-disappearing and disappearing contexts . the trends in interests. at the same time, we want to be made aware of the end or disappearance of entities, such as closing stores or discontinuing services (figure 1 ), as soon as possible so that we can avoid missing out on opportunities or taking fruitless actions after the entities are no longer available. portal sites also need to address this issue by quickly removing unavailable companies, locations, and events from their services. moreover, it is important to collect these entities to maintain knowledge bases @xcite in which information about the entities is continuously accumulated. whereas disappearing entities remain to be discovered, studies on discovering out-of-kb or emerging entities @xcite @xcite have been successful to an extent, as most of these entities have distinct names and can be characterized by mentions to their unseen names. in contrast, we do not have such clues to detect disappearing entities, since most of them continue to be mentioned with the same names even after they actually disappear. given such a situation, we take on the new task of discovering disappearing entities from microblogs where news and personal experiences are widely shared. to detect the entities' disappearance, we exploit the specific expressions that people use when mentioning disappearing entities in microblogs (figure 1 ) ( § 2). by capturing these contexts, we can discover a variety of entities in the early stage even before they disappear. to develop a dataset of disappearing entities and contexts, we use time-sensitive distant supervision @xcite , which collects specific contexts of entities by utilizing kb entities and timestamps of microblogs. because this method requires the timing of the desired contexts, we extract the year of disappearance for each entity described in wikipedia and incorporate it into the distant supervision ( § 4.1). we then train a named entity recognition (ner) model on the collected entities and contexts to discover and type disappearing entities. however, the ner model performs poorly for microblogs @xcite where posts are short and noisy, and the training data collected by the distant supervision also contains noise, making it difficult to train a reliable model. we address this issue by considering that multiple posts are likely to mention the target entity when it disappears. concretely, we utilize these posts to refine pretrained word embeddings and incorporate them into the ner model ( § 4.2). this enables the model to consider the tokens that frequently appear among multiple posts and to recognize disappearing entities robustly. to evaluate our method, we built large-scale english and japanese datasets from twitter using the proposed time-sensitive distant supervision method ( § 5.1). the experimental results ( § 5.4) demonstrated that our method outperformed the baseline, which simply collected the latest burst of posts about the disappearing entities as the disappearing contexts using the original version of time-sensitive distant supervision and used them to train the ner model. in addition, the evaluation of relative recall indicated that our method successfully found more than 70% of the target disappearing entities in wikipedia. except for entities whose disappearances are sudden and receive media exposure (e.g., persons), our method detected entities such as services, facilities, and events on average more than 100 days earlier than the update of the disappearance in wikipedia. ## definition of disappearing entity in this section, we define the meaning of the term disappearing entity in this study. we consider the entities' disappearance to be the disappearance of its existence from the real world or official announcement of its discontinuation, with reference to the list of ending entities in wikipedia. 2 to reach a solid definition of disappearing entities, we refer to the definition of emerging entities @xcite . they reported that emerging entities have a specific process from their first appearance to when they become known to the public; during this process, they appear with specific expressions, i.e., emerging contexts. we define disappearing entities and contexts by focusing on the fact that specific expressions that indicate plans and signs of disappearance appear in contexts not only at the time of disappearance but also in the process leading up to that time as follows: disappearing contexts. contexts in which the writers assumed the readers do not know the disappearance of the entities. non-disappearing contexts. contexts other than disappearing contexts. ## related work there are no studies that detect disappearing entities in a timely manner. we briefly review the existing studies related to our task. ## proposed method to accurately collect contexts of disappearing entities ( § 2), we adopt the framework of time-sensitive distant supervision @xcite , which efficiently collects specific contexts of given entities utilizing microblog timestamps. here, we utilize the timings of the entities' disappearance described in wikipedia to refine the original time-sensitive distant supervision since the original method is designed for emerging entities and it is difficult to apply the method to disappearing entities directly. to ensure that a detection model can make robust predictions using the noisy dataset constructed using distant supervision and for noisy microblog posts, we refine pretrained word embeddings to acquire features from multiple occurrences of disappearing entities and feed them into the detection model. as a microblog, we target twitter, where various sources including news articles and personal posts, are extensively and instantly shared. ## experiments we performed our task of discovering disappearing entities using datasets built from twitter. ## conclusion in this paper, we have introduced the task of discovering disappearing entities in microblogs ( § 1, § 2, and § 3). to deal with the uncertainty of entity disappearance, we considered the year of disappearance in wikipedia and fed it into time-sensitive distant supervision ( § 4.1). to perform the detection from noisy microblog posts, we proposed the method of refining pretrained word embeddings using the twitter stream ( § 4.2). we actually constructed a multi-lingual dataset in japanese and english using the proposed time-sensitive distant supervision ( § 5.1). experimental results ( § 5.4) demonstrated that our method outperformed the baseline method and successfully found more than 70% of the target disappearing entities in wikipedia and they were detected more than a month earlier than the update of the disappearance in wikipedia. we release the dataset used in our experiments. 1 we plan to extract information such as the specific time of disappearance to make the discovered entities more useful for various applications.