This paper describes a computational approach to resolving the true referent of a named mention of a person in the body of an email . A generative model of mention generation is used to guide mention resolution . Results on three relatively small collections indicate that the accuracy of this approach compares favorably to the best known techniques , and results on the full CMU Enron collection indicate that it scales well to larger collections . The increasing prevalence of informal text from which a dialog structure can be reconstructed ( e.g. , email or instant messaging ) , raises new challenges if we are to help users make sense of this cacophony . Large collections offer greater scope for assembling evidence to help with that task , but they pose additional challenges as well . With well over 100,000 unique email addresses in the CMU version of the Enron collection ( Klimt and Yang , 2004 ) , common names ( e.g. , John ) might easily refer to any one of several hundred people . In this paper , we associate named mentions in unstructured text ( i.e. , the body of an email and/or the subject line ) to modeled identities . We see at least two direct applications for this work : ( 1 ) helping searchers who are unfamiliar with the contents of an email collection ( e.g. , historians or lawyers ) better understand the context of emails that they find , and ( 2 ) augmenting more typical social networks ( based on senders and recipients ) with additional links based on references found in unstructured text . Most approaches to resolving identity can be decomposed into four sub-problems : ( 1 ) finding a reference that requires resolution , ( 2 ) identifying candidates , ( 3 ) assembling evidence , and ( 4 ) choosing among the candidates based on the evidence . For the work reported in this paper , we rely on the user to designate references requiring resolution ( which we model as a predetermined set of mention-queries for which the correct referent is known ) . Candidate identification is a computational expedient that permits the evidence assembly effort to be efficiently focused ; we use only simple techniques for that task . Our principal contributions are the approaches we take to evidence generation ( leveraging three ways of linking to other emails where evidence might be found : reply chains , social interaction , and topical similarity ) and our approach to choosing among candidates ( based on a generative model of reference production ) . We evaluate the effectiveness of our approach on four collections , three of which have previously reported results for comparison , and one that is considerably larger than the others . The remainder of this paper is as follows . Section 2 surveys prior work . Section 3 then describes our approach to modeling identity and ranking candidates . Section 4 presents results , and Section 5 concludes . We have presented an approach to mention resolution in email that flexibly makes use of expanding contexts to accurately resolve the identity of a given mention . Our approach focuses on four naturally occurring contexts in email , including a message , a thread , other emails with senders and/or recipients in common , and other emails with significant topical content in common . Our approach outperforms previously reported techniques and it scales well to larger collections . Moreover , our results serve to highlight the importance of social context when resolving mentions in social media , which is an idea that deserves more attention generally . In future work , we plan to extend our test collection with mention queries that must be resolved in the " long tail " of the identity distribution where less evidence is available . We are also interested in exploring iterative approaches to jointly resolving mentions .

Challenge: Increase of the number of informal textual communication such as email or instant messaging cacophony for users.
Approach: They propose a computational approach to the referent of named mentions in the body of an email by decomposing it into three different modules.
Outcome: Experiments with three small and one large datasets show that the proposed approach performs on par with existing models and scales well.