@InProceedings{xu-yang-huang:2016:ALR12,
  author    = {Xu, Ge  and  Yang, Xiaoyan  and  Huang, Chu-Ren},
  title     = {Selective Annotation of Sentence Parts: Identification of Relevant Sub-sentential Units},
  booktitle = {Proceedings of the 12th Workshop on Asian Language Resources (ALR12)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {86--94},
  abstract  = {Many NLP tasks involve sentence-level annotation yet the relevant information
	is not encoded at sentence level but at some relevant parts of the sentence.
	Such tasks include but are not limited to: sentiment expression annotation,
	product feature annotation, and template annotation for Q\&A systems. However,
	annotation of the full corpus sentence by sentence is resource intensive. In
	this paper, we propose an approach that iteratively extracts frequent parts of
	sentences for annotating, and compresses the set of sentences after each round
	of annotation. Our approach can also be used in preparing training sentences
	for binary classification (domain-related vs. noise, subjectivity vs.
	objectivity, etc.), assuming that sentence-type annotation can be predicted by
	annotation of the most relevant sub-sentences. Two experiments are performed to
	test our proposal and evaluated in terms of time saved and agreement of
	annotation.},
  url       = {http://aclweb.org/anthology/W16-5411}
}