@InProceedings{pan-EtAl:2017:I17-1,
  author    = {Pan, Liangming  and  Wang, Xiaochen  and  Li, Chengjiang  and  Li, Juanzi  and  Tang, Jie},
  title     = {Course Concept Extraction in MOOCs via Embedding-Based Graph Propagation},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {875--884},
  abstract  = {Massive Open Online Courses (MOOCs), offering a new way to study online, are
	revolutionizing education. One challenging issue in MOOCs is how to design
	effective and fine-grained course concepts such that students with different
	backgrounds can grasp the essence of the course. In this paper, we conduct a
	systematic investigation of the problem of course concept extraction for MOOCs.
	We propose to learn latent representations for candidate concepts via an
	embedding-based method. Moreover, we develop a graph-based propagation
	algorithm to rank the candidate concepts based on the learned representations.
	We evaluate the proposed method using different courses from XuetangX and
	Coursera. Experimental results show that our method significantly outperforms
	all the alternative methods (+0.013-0.318 in terms of  R-precision; p<<0.01,
	t-test).},
  url       = {http://www.aclweb.org/anthology/I17-1088}
}