Search (2 results, page 1 of 1)

  • × author_ss:"Oard, D.W."
  • × theme_ss:"Internet"
  • × year_i:[2010 TO 2020}
  1. Kang, H.; Plaisant, C.; Elsayed, T.; Oard, D.W.: Making sense of archived e-mail : exploring the Enron collection with NetLens (2010) 0.01 
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    Abstract
    Informal communications media pose new challenges for information-systems design, but the nature of informal interaction offers new opportunities as well. This paper describes NetLens-E-mail, a system designed to support exploration of the content-actor network in large e-mail collections. Unique features of NetLens-E-mail include close coupling of orientation, specification, restriction, and expansion, and introduction and incorporation of a novel capability for iterative projection between content and actor networks within the same collection. Scenarios are presented to illustrate the intended employment of NetLens-E-mail, and design walkthroughs with two domain experts provide an initial basis for assessment of the suitability of the design by scholars and analysts.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.4, S.723-744
    Type
    a
  2. Gao, N.; Dredze, M.; Oard, D.W.: Person entity linking in email with NIL detection (2017) 0.01 
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    Abstract
    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.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.10, S.2412-2424
    Type
    a

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