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.
Our previous work on implicit entity linking has dealt with clinical entities in electronic medical records @cite_14 . The main challenge in the medical setting resides in the heterogeneous usage of language by individuals mentioning entities implicitly including their negated mentions. Our approach focused on modeling the entities by using their static definitions and exploiting WordNet as the knowledge base to account for heterogeneity in language usage. The task of implicit entity linking in tweets is different from that of clinical text since, in addition to the differences in nature of the text, the heterogeneity arise with the usage of different characteristics of entities and their association with dynamic context as discussed in sec:understanding . Hence, the static descriptions definitions of entities fall short in linking implicit entities as we will demonstrate in our evaluation.
@cite_14
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With the increasing automation of health care information processing, it has become crucial to extract meaningful information from textual notes in electronic medical records. One of the key challenges is to extract and normalize entity mentions. State-of-the-art approaches have focused on the recognition of entities that are explicitly mentioned in a sentence. However, clinical documents often contain phrases that indicate the entities but do not contain their names. We term those implicit entity mentions and introduce the problem of implicit entity recognition (IER) in clinical documents. We propose a solution to IER that leverages entity definitions from a knowledge base to create entity models, projects sentences to the entity models and identifies implicit entity mentions by evaluating semantic similarity between sentences and entity models. The evaluation with 857 sentences selected for 8 different entities shows that our algorithm outperforms the most closely related unsupervised solution. The similarity value calculated by our algorithm proved to be an effective feature in a supervised learning setting, helping it to improve over the baselines, and achieving F1 scores of .81 and .73 for different classes of implicit mentions. Our gold standard annotations are made available to encourage further research in the area of IER.