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Table I: The institutional affiliations of the authors

in unknown title

by unknown authors

Table 3: The institutional affiliations of the authors

in unknown title

by unknown authors

Table 2: The institutional affihations of authors on

in unknown title

by unknown authors

Table 5: The institutional affiliations of the authors on the

in unknown title

by unknown authors 1984

Table 31 The institutional rrffihations of the authors

in unknown title

by unknown authors

Table 1: Author disambiguation example with multiple institutions.

in Author Disambiguation using Error-driven Machine Learning with a Ranking Loss Function

by Aron Culotta, Pallika Kanani, Robert Hall, Michael Wick, Andrew Mccallum

"... In PAGE 2: ...Table 2: Author disambiguation example with overlapping co-authors. Motivating Examples Table1 shows three synthetic publication records that demonstrate the difficulty of author disambiguation. Each record contains equivalent author strings, and the publication titles contains similar words (networks, un- derstanding, protocols).... In PAGE 2: ... For example, this function can compute a feature indicating that an author is affiliated with three different institutions in a three year period. Given training data in which this event is rare, it is likely that the classifier would not predict all the au- thors in Table1 to be duplicates. Table 2 shows an example in which co-author infor- mation is available.... ..."