Entity Linking: Detecting Entities within Text

With unstructured text on the web and social media increasing at a furious pace, it is all the more important to develop techniques that can ease semantic understanding of text data for humans. One of the key tasks in this process is that of entity linking; identifying mentions of entities in text. Consider the line that reads “The Prime Minister came under harsh criticism over the Immigration Act 2014” Without any additional context, it is not obvious to humans as to who is being talked about. An entity linking technique that has the entity database at its disposal, however, can easily figure out that the mention PrimeMinister refers to the PrimeMinister of UK since the mention of Immigration Act 2014 in the same sentence narrows down the search space from the set of all countries that have Prime Ministers to just UK. Such linking of text documents to entities enables easier understanding for the reader, as well as improved accuracy in automated tasks such as text document clustering, classification and information retrieval. With the advent of social media, the set of entities that have a presence on the web has increased from just famous places, objects and people, to everyone that has a social media presence, which is to say, virtually the vast majority of human beings. Availability of such a heterogeneous set of entities ranging from those in domain-specific ontologies to social media profiles provides fresh challenges and opportunities for entity linking. In this tutorial, we will cover the set of entity linking techniques that have been proposed in literature over the years, and provide a systematic survey of them with classifications along various dimensions. We will also explore the applicability of entity linking on noisy and short texts, such as those generated in microblogging platforms (ex. Twitter), and elaborate on the new challenges for entity linking that have not quite received enough attention from the scholarly community.