document: Targeted disambiguation of ad-hoc , homogeneous sets of named entities In many entity extraction applications , the entities to be recognized are constrained to be from a list of `` target entities '' . In many cases , these target entities are ( i ) ad-hoc , i.e. , do not exist in a knowledge base and ( ii ) homogeneous ( e.g. , all the entities are IT companies ) . We study the following novel disambiguation problem in this unique setting : given the candidate mentions of all the target entities , determine which ones are true mentions of a target entity . Prior techniques only consider target entities present in a knowledge base and\/or having a rich set of attributes . In this paper , we develop novel techniques that require no knowledge about the entities except their names . Our main insight is to leverage the homogeneity constraint and disambiguate the candidate mentions collectively across all documents . We propose a graph-based model , called MentionRank , for that purpose . Furthermore , if additional knowledge is available for some or all of the entities , our model can leverage it to further improve quality . Our experiments demonstrate the effectiveness of our model . To the best of our knowledge , this is the first work on targeted entity disambiguation for ad-hoc entities .
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