Person entity linking in email with NIL detection

Ning Gao, Mark Dredze, Douglas W. Oard

Research output: Contribution to journalArticlepeer-review


For each specific mention of an entity found in a text, the goal of entity linking is to determine whether the referenced entity is present in an existing knowledge base, and if so to determine which KB entity is the correct referent. Entity linking has been well explored for dissemination-oriented sources such as news stories, blogs, and microblog posts, but the limited work to date on “conversational” sources such as email or text chat has not yet attempted to determine when the referent entity is not in the knowledge base (a task known as “NIL detection”). This article presents a supervised machine learning system for linking named mentions of people in email messages to a collection-specific knowledge base, and that is also capable of NIL detection. This system learns from manually annotated training examples to leverage a rich set of features. The entity linking accuracy for entities present in the knowledge base is substantially and significantly better than the best previously reported results on the Enron email collection, comparable accuracy is reported for the challenging NIL detection task, and these results are for the first time replicated on a second email collection from a different source with comparable results.

Original languageEnglish (US)
Pages (from-to)2412-2424
Number of pages13
JournalJournal of the Association for Information Science and Technology
Issue number10
StatePublished - Oct 2017
Externally publishedYes

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management
  • Library and Information Sciences


Dive into the research topics of 'Person entity linking in email with NIL detection'. Together they form a unique fingerprint.

Cite this