  When persons are mentioned in texts with their first name, last name and/or
middle names, there can be a high variation which of their names are used, how
their names are ordered and if their names are abbreviated. If multiple persons
are mentioned consecutively in very different ways, especially short texts can
be perceived as "messy". Once ambiguous names occur, associations to persons
may not be inferred correctly. Despite these eventualities, in this paper we
ask how well an unsupervised algorithm can build a person index from short
texts. We define a person index as a structured table that distinctly catalogs
individuals by their names. First, we give a formal definition of the problem
and describe a procedure to generate ground truth data for future evaluations.
To give a first solution to this challenge, a baseline approach is implemented.
By using our proposed evaluation strategy, we test the performance of the
baseline and suggest further improvements. For future research the source code
is publicly available.
