@InProceedings{wilson-mihalcea:2017:I17-1,
  author    = {Wilson, Steven  and  Mihalcea, Rada},
  title     = {Measuring Semantic Relations between Human Activities},
  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     = {664--673},
  abstract  = {The things people do in their daily lives can provide valuable insights into
	their personality, values, and interests. Unstructured text data on social
	media platforms are rich in behavioral content, and automated systems can be
	deployed to learn about human activity on a broad scale if these systems are
	able to reason about the content of interest. In order to aid in the evaluation
	of such systems, we introduce a new phrase-level semantic textual similarity
	dataset comprised of human activity phrases, providing a testbed for automated
	systems that analyze relationships between phrasal descriptions of people's
	actions. Our set of 1,000 pairs of activities is annotated by human judges
	across four relational dimensions including similarity, relatedness,
	motivational alignment, and perceived actor congruence. We evaluate a set of
	strong baselines for the task of generating scores that correlate highly with
	human ratings, and we introduce several new approaches to the phrase-level
	similarity task in the domain of human activities.},
  url       = {http://www.aclweb.org/anthology/I17-1067}
}