@InProceedings{glaser-kuhn:2016:COLING,
  author    = {Glaser, Andrea  and  Kuhn, Jonas},
  title     = {Named Entity Disambiguation for little known referents: a topic-based approach},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1481--1492},
  abstract  = {We propose an approach to Named Entity Disambiguation that avoids a problem of
	standard work on the task (likewise affecting fully supervised, weakly
	supervised, or distantly supervised machine learning techniques): the treatment
	of name mentions referring to people with no (or very little) coverage in the
	textual training data is systematically incorrect. We propose to indirectly
	take into account the property information for the "non-prominent" name
	bearers, such as nationality and profession (e.g., for a Canadian law professor
	named Michael Jackson, with no Wikipedia article, it is very hard to obtain
	reliable textual training data). The target property information for the
	entities is directly available from name authority files, or inferrable, e.g.,
	from listings of sportspeople etc. Our proposed approach employs topic modeling
	to exploit textual training data based on entities sharing the relevant
	properties. In experiments with a pilot implementation of the general approach,
	we show that the approach does indeed work well for name/referent pairs with
	limited textual coverage in the training data.},
  url       = {http://aclweb.org/anthology/C16-1140}
}