Over the years, Twitter has become one of the largest communication platforms providing key data to various applications such as brand monitoring, trend detection, among others. Entity linking is one of the major tasks in natural language understanding from tweets and it associates entity mentions in text to corresponding entries in knowledge bases in order to provide unambiguous interpretation and additional context. State-of-the-art techniques have focused on linking explicitly mentioned entities in tweets with reasonable success. However, we argue that in addition to explicit mentions --- i.e. 'The movie Gravity was more expensive than the mars orbiter mission' --- entities movie Gravity can also be mentioned implicitly --- i.e. 'This new space movie is crazy. you must watch it!.' This paper introduces the problem of implicit entity linking in tweets. We propose an approach that models the entities by exploiting their factual and contextual knowledge. We demonstrate how to use these models to perform implicit entity linking on a ground truth dataset with 397 tweets from two domains, namely, Movie and Book. Specifically, we show: 1 the importance of linking implicit entities and its value addition to the standard entity linking task, and 2 the importance of exploiting contextual knowledge associated with an entity for linking their implicit mentions. We also make the ground truth dataset publicly available to foster the research in this new research areacity.
The word-level tweet linking has two main steps: The word-level entity linking has been studied extensively for text like Wikipedia and News @cite_10 @cite_12 @cite_4 @cite_1 @cite_7 ; however, these approaches have proved to be ineffective on short and noisy text like tweets @cite_2 @cite_8 . Here we will discuss the approaches taken to solve this problem in tweets. The first step was performed by matching the word sequences of the tweet to the page titles and the anchor texts in Wikipedia and consider all matching pages and pages redirected by matching anchor texts to be candidates @cite_11 @cite_8 @cite_6 . The second step was performed by optimizing the relatedness calculated among the candidate entities @cite_8 @cite_5 or based on the threshold defined over measures that calculate the similarity between the entity mention and entity representation @cite_11 , or by applying structural learning techniques @cite_6 . An approach to solve the implicit entity linking in tweets has to take a fresh perspective since by definition it has neither anchor text nor the page title present in the tweet.
@cite_4 @cite_7 @cite_8 @cite_1 @cite_6 @cite_2 @cite_5 @cite_10 @cite_12 @cite_11
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We study the task of entity linking for tweets, which tries to associate each mention in a tweet with a knowledge base entry. Two main challenges of this task are the dearth of information in a single tweet and the rich entity mention variations. To address these challenges, we propose a collective inference method that simultaneously resolves a set of mentions. Particularly, our model integrates three kinds of similarities, i.e., mention-entry similarity, entry-entry similarity, and mention-mention similarity, to enrich the context for entity linking, and to address irregular mentions that are not covered by the entity-variation dictionary. We evaluate our method on a publicly available data set and demonstrate the effectiveness of our method. Twitter has become an increasingly important source of information, with more than 400 million tweets posted per day. The task to link the named entity mentions detected from tweets with the corresponding real world entities in the knowledge base is called tweet entity linking. This task is of practical importance and can facilitate many different tasks, such as personalized recommendation and user interest discovery. The tweet entity linking task is challenging due to the noisy, short, and informal nature of tweets. Previous methods focus on linking entities in Web documents, and largely rely on the context around the entity mention and the topical coherence between entities in the document. However, these methods cannot be effectively applied to the tweet entity linking task due to the insufficient context information contained in a tweet. In this paper, we propose KAURI, a graph-based framework to collectively link all the named entity mentions in all tweets posted by a user via modeling the user's topics of interest. Our assumption is that each user has an underlying topic interest distribution over various named entities. KAURI integrates the intra-tweet local information with the inter-tweet user interest information into a unified graph-based framework. We extensively evaluated the performance of KAURI over manually annotated tweet corpus, and the experimental results show that KAURI significantly outperforms the baseline methods in terms of accuracy, and KAURI is efficient and scales well to tweet stream. A major challenge in Entity Linking (EL) is making effective use of contextual information to disambiguate mentions to Wikipedia that might refer to different entities in different contexts. The problem exacerbates with cross-lingual EL which involves linking mentions written in non-English documents to entries in the English Wikipedia: to compare textual clues across languages we need to compute similarity between textual fragments across languages. In this paper, we propose a neural EL model that trains fine-grained similarities and dissimilarities between the query and candidate document from multiple perspectives, combined with convolution and tensor networks. Further, we show that this English-trained system can be applied, in zero-shot learning, to other languages by making surprisingly effective use of multi-lingual embeddings. The proposed system has strong empirical evidence yielding state-of-the-art results in English as well as cross-lingual: Spanish and Chinese TAC 2015 datasets. Information extraction from microblog posts is an important task, as today microblogs capture an unprecedented amount of information and provide a view into the pulse of the world. As the core component of information extraction, we consider the task of Twitter entity linking in this paper. In the current entity linking literature, mention detection and entity disambiguation are frequently cast as equally important but distinct problems. However, in our task, we find that mention detection is often the performance bottleneck. The reason is that messages on micro-blogs are short, noisy and informal texts with little context, and often contain phrases with ambiguous meanings. To rigorously address the Twitter entity linking problem, we propose a structural SVM algorithm for entity linking that jointly optimizes mention detection and entity disambiguation as a single end-to-end task. By combining structural learning and a variety of firstorder, second-order, and context-sensitive features, our system is able to outperform existing state-of-the art entity linking systems by 15 F1.