@inproceedings{perez-rosas-etal-2017-predicting,
title = "Predicting Counselor Behaviors in Motivational Interviewing Encounters",
author = "P{\'e}rez-Rosas, Ver{\'o}nica and
Mihalcea, Rada and
Resnicow, Kenneth and
Singh, Satinder and
An, Lawrence and
Goggin, Kathy J. and
Catley, Delwyn",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-1106",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Predicting Counselor Behaviors in Motivational Interviewing Encounters
%A Pérez-Rosas, Verónica
%A Mihalcea, Rada
%A Resnicow, Kenneth
%A Singh, Satinder
%A An, Lawrence
%A Goggin, Kathy J.
%A Catley, Delwyn
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F perez-rosas-etal-2017-predicting
%X 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.
%U https://aclanthology.org/E17-1106
%P 1128-1137
Markdown (Informal)
[Predicting Counselor Behaviors in Motivational Interviewing Encounters](https://aclanthology.org/E17-1106) (Pérez-Rosas et al., EACL 2017)
ACL
- Verónica Pérez-Rosas, Rada Mihalcea, Kenneth Resnicow, Satinder Singh, Lawrence An, Kathy J. Goggin, and Delwyn Catley. 2017. Predicting Counselor Behaviors in Motivational Interviewing Encounters. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1128–1137, Valencia, Spain. Association for Computational Linguistics.