Abstract

Named Entity Linking attempts to ground textual entity mentions to an external knowledge base (e.g., Wikipedia). Mentions are assigned a KB entry or NIL if they are absent from the KB. The task requires resolving name polysemy and synonymy to disambiguate their references. I’ll discuss the NEL task, datasets and evaluation as well as a comparison of some systems from the literature.

More recently, we’ve investigated how to better extract entity descriptions for disambiguation. While apposition is used as a component in several tasks (e.g., Coreference Resolution, Textual Entailment), apposition extraction performance is not often directly evaluated. We propose systems exploiting syntactic and semantic constraints to extract appositions from OntoNotes 4. Our joint log-linear model outperforms the state-of-the-art model (Favre and Hakkani-Tür in Interspeech 2009), by around 10% on Broadcast News, and achieves 54.3% F-score on multiple genres. I’ll talk about our apposition system and some more general work on sentence-local description and its application to NEL .

Finally, I’ll demonstrate an application developed with our industry partner. Fairfax Media operates some of the main metropolitan newspapers and news websites in Australia and has recently launched “zoom”, showing how NEL can provide a compelling view of 25 years of stories.