Exploiting Lexical & Encyclopedic Resources For Entity Disambiguation

Entity disambiguation is the problem of determining whether two mentions of entities refer to the same object: e.g., trying to decide whether the entity called “Jim Clark” in one document is the same as the entity called “Jim Clark” in another document. To do this accurately, it is necessary to extract from these documents descriptions of these entities as exhaustive and accurate as possible. This in turn requires ‘tracking’ these entities in each document – identifying all or most of their mentions – and collecting their properties, particularily those that help the most to discriminate between individuals.

The goal of the workshop is to further the state of the art in entity disambiguation by developing better techniques for tracking entities and for extracting their properties. A particular focus will be improving entity tracking by using lexical and encyclopedic knowledge extracted both from structured lexical databases and from semi-strcutured repositories such as Wikipedia. Lack of such knowledge is one of the main problems with current entity tracking methods, which typically cannot detect that ‘the Packwood proposal’ and ‘the Packwood plan’ in the following example refer to the same entity.

  • [The Packwood proposal] would reduce the tax depending on how long an asset was held. It also would create a new IRA that would shield from taxation the appreciation on investments made for a wide variety of purposes, including retirement, medical expenses, first-home purchases and tuition.
  • A White House spokesman said President Bush is “generally supportive” of [the Packwood plan]

Methods to be used include text mining techniques (supervised and unsupervised) to extract object properties; better machine learning techniques to improve entity tracking (e.g., using tree kernels); methods for extracting knowledge from WordNet, semantic role labellers, and Wikipedia; and clustering methods for entity disambiguation.

Entity Disambiguation Scoring Metrics
SVMs and Kernels
Versley System – PDF


Team Members
Senior Members
Ron ArtsteinUniversity of Essex
David DayMITRE
Jason DuncanDepartment of Defense
Alessandro MoschittiUniversity of Trento
Massimo PoesioUnversity of Essex and University of Trento
Xiaofeng YangInstitute for Infocomm Research, Singapore
Graduate Students
Jason SmithCLSP
Robert HallUniversity of Massachussetts
Simone PonzettoEML Research
Yannick VersleyUniversity of Tubingen
Michael WickUniversity of Massachusetts
Undergraduate Students
Vladimir EidelmanColumbia University
Alan JernUniversity of California Los Angeles
Brett ShwomNew York University
Affiliate Members
Walter DaelmansUniversity of Antwerp
Claudio GiulianoFBK-IRST
Janet HitzemanMITRE
Veronique HosteUniversity of Antwerp
Emily JamisonOhio
Mijail KabadjovEdinburgh University
Gideon MannUniversity of Massachusetts
Sameer PradhanBBN
Michael StrubeEML Research

Center for Language and Speech Processing