@InProceedings{amplayo-song:2016:BioTxtM2016,
  author    = {Amplayo, Reinald Kim  and  Song, Min},
  title     = {Building Content-driven Entity Networks for Scarce Scientific Literature using Content Information},
  booktitle = {Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {20--29},
  abstract  = {This paper proposes several network construction methods for collections of
	scarce scientific literature data. We define scarcity as lacking in value and
	in volume. Instead of using the paper's metadata to construct several kinds of
	scientific networks, we use the full texts of the articles and automatically
	extract the entities needed to construct the networks. Specifically, we present
	seven kinds of networks using the proposed construction methods: co-occurrence
	networks for author, keyword, and biological entities, and citation networks
	for author, keyword, biological, and topic entities. We show two case studies
	that applies our proposed methods: CADASIL, a rare yet the most common form of
	hereditary stroke disorder, and Metformin, the first-line medication to the
	type 2 diabetes treatment. We apply our proposed method to four different
	applications for evaluation: finding prolific authors, finding important
	bio-entities, finding meaningful keywords, and discovering influential topics.
	The results show that the co-occurrence and citation networks constructed using
	the proposed method outperforms the traditional-based networks. We also compare
	our proposed networks to traditional citation networks constructed using enough
	data and infer that even with the same amount of enough data, our methods
	perform comparably or better than the traditional methods.},
  url       = {http://aclweb.org/anthology/W16-5103}
}