@InProceedings{perezrosas-EtAl:2017:EACLlong,
  author    = {P\'{e}rez-Rosas, Ver\'{o}nica  and  Mihalcea, Rada  and  Resnicow, Kenneth  and  Singh, Satinder  and  Ann, Lawrence  and  Goggin, Kathy J.  and  Catley, Delwyn},
  title     = {Predicting Counselor Behaviors in Motivational Interviewing Encounters},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {1128--1137},
  abstract  = {As the number of people receiving psycho-therapeutic treatment increases, the
	automatic evaluation of counseling practice arises as an important challenge in
	the clinical domain. In this paper, we address the automatic evaluation of
	counseling performance by analyzing counselors' language during their
	interaction with clients. In particular, we present a model towards the
	automation of Motivational Interviewing (MI) coding, which is the current gold
	standard to evaluate MI counseling. First, we build a dataset of hand labeled
	MI encounters; second, we use text-based methods to extract and analyze
	linguistic patterns associated with counselor behaviors; and third, we develop
	an automatic system to predict these behaviors. We introduce a new set of
	features based on semantic information and syntactic patterns, and show that
	they lead to accuracy figures of up to 90%, which represent a significant
	improvement with respect to features used in the past.},
  url       = {http://www.aclweb.org/anthology/E17-1106}
}