  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 con- text. 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 ex- pensive 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 area.
