Fine-grained Typing of Emerging Entities in Microblogs (2021)

Abstract: Analyzing microblogs where we post what we experience enables us to perform various applications such as social-trend analysis and entity recommendation. To track emerging trends in a variety of areas, we want to categorize information on emerging entities (e.g., Avatar 2) in microblog posts according to their types (e.g., Film). We thus introduce a new entity typing task that assigns a fine-grained type to each emerging entity when a burst of posts containing that entity is first observed in a microblog. The challenge is to perform typing from noisy microblog posts without relying on prior knowledge of the target entity. To tackle this task, we build large-scale Twitter datasets for English and Japanese using time-sensitive distant supervision. We then propose a modular neural typing model that encodes not only the entity and its contexts but also meta information in multiple posts. To type ‘homographic’ emerging entities (e.g., ‘Go’ means an emerging programming language and a classic board game), which contexts are noisy, we devise a context selector that finds related contexts of the target entity. Experiments on the Twitter datasets confirm the effectiveness of our typing model and the context selector.

## introduction microblogs enable us to instantly share a wider variety of topics than news streams @xcite and have become one of the primary sources for acquiring new information. to analyze this huge volume of posts for applications such as social-trend analysis and entity recommendation, it is necessary to extract entity units from them and classify their types using techniques such as named entity recognition (ner) and entity linking @xcite . however, newly 'emerging' entities (e.g., avatar 2) are difficult to handle because they do not exist in the training data of supervised models or the knowledge bases (kbs), and valuable information of the entities is often thrown away. motivated by this background, @xcite ( § 2.1) defined emerging entities as that appear in contexts that emphasize their novelty, and attempted to discover emerging entities from microblogs. to extract emerging entities, they exploited the fact that entities appear in characteristic contexts when they first emerge (e.g., new games often appear with "trailer," "release" and a console name (figure 1 )) ( § 3.1), and developed a method of discovering them from microblogs. although their method detected emerging entities promptly, typing those emerging entities is still necessary for usage in the downstream applications. existing studies on entity typing, however, focus on non-emerging (or prevalent) entities @xcite @xcite @xcite ) ( § 2.2). most of them classify single mentions of entities into their context-dependent types. to complement a scarce context, many studies rely on language resources such as kbs to narrow down the candidate types. unfortunately, those resources are not available for newly appearing entities. it is unrealistic to perform accurate mention-level typing using these methods in a short and noisy microblog post. we thus design a task of identifying a finegrained entity type from a burst of posts about the target entity (figure 1 , § 3.2), assuming that the target mention is detected in advance. this is a more realistic setting for typing emerging entities than the conventional mention-level typing. to build training data for this task ( § 3.3), we collect emerging entities and their contexts for english and japanese using distant supervision @xcite . to evaluate typing methods, we manually build test data for two types of emerging entities: homographic and nonhomographic; homographic entities share names with other words (e.g., 'go' for a board game, a programming language, and a verb) and consequently their contexts are contaminated. we then propose a modular entity typing model that performs multi-instance (mi) learning @xcite ( § 4.1). in addition to contexts for the entity and its entity surface, this model leverages meta-information such as urls and usernames, exploiting the characteristics of the microblog domain. because entities can have homographs, it is risky to use all the posts obtained using simple string matching as contexts for typing. we thus propose to find and use emerging contexts since two emerging entities with the same name are unlikely to emerge in a short period of time and such contexts are useful for typing ( § 4.2). we finally evaluate our typing model on the above english and japanese twitter datasets ( § 5). experimental results confirm that our model outperforms a baseline model that performs mi-learning with randomly selected posts in training and testing. we demonstrate that when typing homographic emerging entities, it is more important to selectively use emerging contexts and meta information. our contributions are as follows: ## related work in this section, we first review existing studies on the definition and detection of the emerging entities. we then explain the existing task settings of entity typing and discuss their limitations. ## task and datasets this section first introduces the definition of emerging entities @xcite and then defines our task of typing emerging entities. finally, we describe our dataset for this task. ## proposed method this section presents a method for typing emerging entities in microblogs. microblogs have the following characteristics: most posts are short and noisy, several posts about the same topic appear in close time series, and it has meta-information such as usernames and urls that are useful for inferring the type. we thus develop a neural typing model based on diverse features and mi-learning ( § 4.1). considering the existence of homographic entities (e.g., go), one may want to select only the posts that are relevant to the target entity, rather than using all posts when performing mi-learning. we thus develop a context selection model that ranks emerging contexts of the target entity ( § 4.2). in the following, we describe the details of each model and how to train and test the models. ## experiments we performed emerging entity typing using the english and japanese twitter datasets built in § 3.3. ## conclusions we introduced a task of typing emerging entities in microblogs ( § 3.2). to perform this task, on the basis of the definition of emerging entities ( § 3.1), we constructed large-scale twitter datasets for english and japanese ( § 3.3). we developed a modular entity typing model ( § 4.1) that encodes different aspects of an emerging entity with mi-learning. to deal with noisy contexts of homographic entities, we adopt a context selection model ( § 4.2) that differentiates emerging contexts from others. experiments ( § 5) demonstrated that our method performed more accurately than the baseline model for both non-homographic and homographic emerging entities. we confirmed the importance of selectively using emerging contexts for training and testing the typing model and verified the effectiveness of each network of the proposed typing model. for future work, we plan to perform further profiling of emerging entities such as relation extraction to organize emerging and existing knowledge. we release the dataset used in our experiments. 9