@InProceedings{yoon-kim:2017:I17-2,
  author    = {Yoon, Byeong-Hun  and  Kim, Yu-Seop},
  title     = {Correlation Analysis of Chronic Obstructive Pulmonary Disease (COPD) and its Biomarkers Using the Word Embeddings},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {337--342},
  abstract  = {It is very costly and time consuming to find new biomarkers for specific
	diseases in clinical laboratories. In this study, to find new biomarkers most
	closely related to Chronic Obstructive Pulmonary Disease (COPD), which is
	widely known as respiratory disease, biomarkers known to be associated with
	respiratory diseases and COPD itself were converted into word embedding. And
	their similarities were measured. We used Word2Vec, Canonical Correlation
	Analysis (CCA), and Global Vector (GloVe) for word embedding. In order to
	replace the clinical evaluation, the titles and abstracts of papers retrieved
	from Google Scholars were analyzed and quantified to estimate the performance
	of the word em-bedding models.},
  url       = {http://www.aclweb.org/anthology/I17-2057}
}