{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T14:39:50Z","timestamp":1775486390688,"version":"3.50.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031171130","type":"print"},{"value":"9783031171147","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-17114-7_23","type":"book-chapter","created":{"date-parts":[[2022,9,17]],"date-time":"2022-09-17T11:05:35Z","timestamp":1663412735000},"page":"241-251","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Automated Utterance Labeling of Conversations Using Natural Language Processing"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0369-387X","authenticated-orcid":false,"given":"Maria","family":"Laricheva","sequence":"first","affiliation":[]},{"given":"Chiyu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Guanyu","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Terence","family":"Tracey","sequence":"additional","affiliation":[]},{"given":"Richard","family":"Young","sequence":"additional","affiliation":[]},{"given":"Giuseppe","family":"Carenini","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,18]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"Can, D., Mar\u00edn, R.A., Georgiou, P.G., Imel, Z.E., Atkins, D.C., Narayanan, S.S.: \u201cIt sounds like...\u201d: a natural language processing approach to detecting counselor reflections in motivational interviewing. J. Couns. Psychol. 63(3), 343\u2013350 (2016)","DOI":"10.1037\/cou0000111"},{"key":"23_CR2","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.jsat.2016.01.006","volume":"65","author":"M Tanana","year":"2016","unstructured":"Tanana, M., Hallgren, K.A., Imel, Z.E., Atkins, D.C., Srikumar, V.: A comparison of natural language processing methods for automated coding of motivational interviewing. J. Subst. Abuse Treat. 65, 43\u201350 (2016)","journal-title":"J. Subst. Abuse Treat."},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Lee, F.-T., Hull, D., Levine, J., Ray, B., McKeown, K.: Identifying therapist conversational actions across diverse psychotherapeutic approaches. In: Proceedings of the SLPsych, pp, 12\u201323 (2019)","DOI":"10.18653\/v1\/W19-3002"},{"issue":"1","key":"23_CR4","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1186\/1748-5908-9-49","volume":"9","author":"DC Atkins","year":"2014","unstructured":"Atkins, D.C., Steyvers, M., Imel, Z.E., Smyth, P.: Scaling up the evaluation of psychotherapy: evaluating motivational interviewing fidelity via statistical text classification. Implement. Sci. 9(1), 49 (2014)","journal-title":"Implement. Sci."},{"issue":"1","key":"23_CR5","first-page":"508","volume":"13","author":"J Gibson","year":"2022","unstructured":"Gibson, J., Atkins, D.C., Creed, T.A., Imel, Z., Georgiou, P., Narayanan, S.: Multi-label multi-task deep learning for behavioral coding. IEEE TAC 13(1), 508\u2013518 (2022)","journal-title":"IEEE TAC"},{"issue":"1","key":"23_CR6","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1037\/a0036841","volume":"52","author":"ZE Imel","year":"2015","unstructured":"Imel, Z.E., Steyvers, M., Atkins, D.C.: Computational psychotherapy research: scaling up the evaluation of patient\u2013provider interactions. Psychotherapy 52(1), 19\u201330 (2015)","journal-title":"Psychotherapy"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Park, S., Kim, D., Oh, A.: Conversation model fine-tuning for classifying client utterances in counseling dialogues. arXiv preprint arXiv:1904.00350 (2019)","DOI":"10.18653\/v1\/N19-1148"},{"key":"23_CR8","unstructured":"Valach, L., Young, R. A., Lynam, M.J.: Action Theory: A Primer for Applied Research in the Social Sciences. Greenwood Publishing Group (2002)"},{"key":"23_CR9","unstructured":"Acheampong, F.A., Nunoo-Mensah, H., Chen, W.: Transformer models for text-based emotion detection: a review of BERT-based approaches. Artif. Intell. Rev. (2016)"},{"issue":"3","key":"23_CR10","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1177\/2167696814559304","volume":"3","author":"RA Young","year":"2015","unstructured":"Young, R.A., et al.: Transition to adulthood as a peer project. Emerg. Adulthood 3(3), 166\u2013178 (2015)","journal-title":"Emerg. Adulthood"},{"issue":"1","key":"23_CR11","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.adolescence.2016.11.001","volume":"54","author":"RA Young","year":"2017","unstructured":"Young, R.A., Marshall, S.K., Murray, J.: Prospective content in the friendship conversations of young adults. J. Adolesc. 54(1), 9\u201317 (2017)","journal-title":"J. Adolesc."},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"Yates, A., Cohan, A., Goharian, N.: Depression and self-harm risk assessment in online forums. arXiv preprint arXiv:1709.01848 (2017)","DOI":"10.18653\/v1\/D17-1322"},{"key":"23_CR13","unstructured":"Jurafsky, D.:\u00a0Speech & Language Processing. Pearson Education India (2000)"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Plutchik, R.: A general psychoevolutionary theory of emotion. In: Theories of Emotion, pp. 3\u201333. Academic Press (1980)","DOI":"10.1016\/B978-0-12-558701-3.50007-7"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the ICML 2008, pp. 160\u2013167 (2008)","DOI":"10.1145\/1390156.1390177"},{"key":"23_CR16","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint, arXiv:1907.11692 (2019)"},{"key":"23_CR17","unstructured":"Scikit-learn. https:\/\/hal.inria.fr\/hal-00650905. Accessed 10 June 2022"},{"key":"23_CR18","doi-asserted-by":"crossref","unstructured":"Ma, X., Xu, P., Wang, Z., Nallapati, R., Xiang, B.: Domain adaptation with BERT-based domain classification and data selection. In: Proceedings of DeepLo 2019, pp. 76\u201383 (2019)","DOI":"10.18653\/v1\/D19-6109"},{"key":"23_CR19","unstructured":"Alexander Street. https:\/\/alexanderstreet.com\/. Accessed 15 June 2022"}],"container-title":["Lecture Notes in Computer Science","Social, Cultural, and Behavioral Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-17114-7_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T07:05:28Z","timestamp":1726729528000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17114-7_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031171130","9783031171147"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17114-7_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"18 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SBP-BRiMS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pittsburgh, PA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sbp2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/sbp-brims.org\/2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"50","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"50% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}