@article{
 title = {Person Name Disambiguation by Bootstrapping},
 type = {article},
 year = {2010},
 identifiers = {[object Object]},
 pages = {10-17},
 websites = {http://portal.acm.org/citation.cfm?id=1835449.1835454},
 publisher = {ACM},
 id = {add55afd-6485-3132-b659-614fbc27aff7},
 created = {2012-02-28T00:51:15.000Z},
 file_attached = {true},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 tags = {personal name disambiguation},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Yoshida2010},
 private_publication = {false},
 abstract = {In this paper, we report our system that disambiguates person names in Web search results. The system uses named entities, compound key words, and URLs as features for document similarity calculation, which typically show high precision but low recall clustering results. We propose to use a two-stage clustering algorithm by bootstrapping to improve the low recall values, in which clustering results of the first stage are used to extract features used in the second stage clustering. Experimental results revealed that our algorithm yields better score than the best systems at the latest WePS workshop.},
 bibtype = {article},
 author = {Yoshida, Minoru},
 journal = {Search}
}

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