@InProceedings{warikoo-chang-hsu:2017:DDDSM,
  author    = {Warikoo, Neha  and  Chang, Yung-Chun  and  Hsu, Wen-Lian},
  title     = {Chemical-Induced Disease Detection Using Invariance-based Pattern Learning Model},
  booktitle = {Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Association for Computational Linguistics},
  pages     = {57--64},
  abstract  = {In this work, we introduce a novel feature engineering approach named
	“algebraic invariance” to identify discriminative patterns for learning
	relation pair features for the chemical-disease relation (CDR) task of
	BioCreative V. Our method exploits the existing structural similarity of the
	key concepts of relation descriptions from the CDR corpus to generate robust
	linguistic patterns for SVM tree kernel-based learning. Preprocessing of the
	training data classifies the entity pairs as either related or unrelated to
	build instance types for both inter-sentential and intra-sentential scenarios.
	An invariant function is proposed to process and optimally cluster similar
	patterns for both positive and negative instances. The learning model for CDR
	pairs is based on the SVM tree kernel approach, which generates feature trees
	and vectors and is modeled on suit- able invariance based patterns, bringing
	brevity, precision and context to the identifier features. Results demonstrate
	that our method outperformed other compared approaches, achieved a high recall
	rate of 85.08%, and averaged an F1- score of 54.34% without the use of any
	additional knowledge bases.},
  url       = {http://www.aclweb.org/anthology/W17-5809}
}