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Data Mining and Knowledge Discovery
Published in: Data Mining and Knowledge Discovery 5-6/2014

01-09-2014

Learning about meetings

Authors: Been Kim, Cynthia Rudin

Published in: Data Mining and Knowledge Discovery | Issue 5-6/2014

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Abstract

Most people participate in meetings almost every day, multiple times a day. The study of meetings is important, but also challenging, as it requires an understanding of social signals and complex interpersonal dynamics. Our aim in this work is to use a data-driven approach to the science of meetings. We provide tentative evidence that: (i) it is possible to automatically detect when during the meeting a key decision is taking place, from analyzing only the local dialogue acts, (ii) there are common patterns in the way social dialogue acts are interspersed throughout a meeting, (iii) at the time key decisions are made, the amount of time left in the meeting can be predicted from the amount of time that has passed, (iv) it is often possible to predict whether a proposal during a meeting will be accepted or rejected based entirely on the language (the set of persuasive words) used by the speaker.
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Appendix
Available only for authorised users
Footnotes
1
More details of definition of dialogue acts can be found in (McCowan et al. 2005).
 
2
We also varied \(C_1\) and \(C_2\) and the results were consistent. While doing so, we kept \(C_1\) below 5 so that each reduction in the size of the template costs us no more than 5 in edit distance. We kept \(C_2\) below 0.5 to ensure that we would not remove a backwards arrow to sacrifice accuracy.
 
Literature
Agrawal R, Gunopulos D, Leymann F (1998) Mining process models from workflow logs. Springer, Heidelberg
Banerjee S, Rudnicky AI (2004) Using simple speech based features to detect the state of a meeting and the roles of the meeting participants. In: Proceedings of international conference on spoken language processing, pp 221–231
Black D (1948) On the rationale of group decision-making. J Polit Econ 56(1):23–34CrossRef
Bousmalis K, Mehu M, Pantic M (2009) Spotting agreement and disagreement: a survey of nonverbal audiovisual cues and tools. In: Proceedings of 3rd international conference on affective computing and intelligent interaction and workshops, pp 1–9
Bui TH, Frampton M, Dowding J, Peters S (2009) Extracting decisions from multi-party dialogue using directed graphical models and semantic similarity. In: Proceedings of the 10th special interest group on discourse and dialogue workshop on discourse and dialogue, pp 235–243
Carnegie D (1936) How to win friends and influence people. Simon & Schuster, New York
Davis JH (1973) Group decision and social interaction: a theory of social decision schemes. Psychol Rev 80(2):97–125CrossRef
Eddy SR (1998) Profile hidden markov models. Bioinformatics 14(9):755–763CrossRef
Ertekin S, Rudin C (2011) On equivalence relationships between classification and ranking algorithms. J Mach Learn Res 12:2905–2929MATHMathSciNet
Eugenio BD, Jordan PW, Thomason JH, Moore JD (1999) The agreement process: an empirical investigation of human-human computer-mediated collaborative dialogues. Int J Hum Comput Stud 53(6):1017–1076CrossRef
Fernández R, Frampton M, Ehlen P, Purver M, Peters S (2008) Modeling and detecting decisions in multi-party dialogue. In: Proceedings of the 9th special interest group on discourse and dialogue workshop on discourse and dialogue, pp 156–163
Galley M, McKeown K, Fosler-Lussier E, Jing H (2003) Discourse segmentation of multi-party conversation. In: Proceedings of the 41st annual meeting of association for, computational linguistics, pp 562–569
Green SG, Taber TD (1980) The effects of three social decision schemes on decision group process. Organ Behav Hum Perform 25(1):97–106CrossRef
Guerini M, Strapparava C, Stock O (2008) Trusting politicians’ words (for persuasive NLP). In: Computational linguistics and intelligent text processing, pp 263–274
Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1–3):389–422CrossRefMATH
Hahn S, Ladner R, Ostendorf M (2006) Agreement/disagreement classification: exploiting unlabeled data using contrast classifiers. In: Proceedings of the human language technology conference of the North American chapter of the association for computational linguistics, pp 53–56
Hall J (1994) Americans know how to be productive if managers will let them. Organ Dyn 33:33–46CrossRef
Hillard D, Ostendorf M (2003) Detection of agreement vs. disagreement in meetings: training with unlabeled data. In: Proceedings of the human language technology conference of the North American chapter of the association for computational linguistics
Hsueh PY, Moore J (2008) Automatic decision detection in meeting speech. In: Machine Learning for Multimodal Interaction, pp 168–179
Ji G, Bilmes J (2005) Dialog act tagging using graphical models. In: Proceedings of international conference on acoustics, speech, and signal processing, vol 1, pp 33–36
Kayser TA (1990) Mining group gold: how to cash in on the collaborative brain power of a group. Serif Publishing, El Segundo
Lyngsø RB, Pedersen CNS (2002) The consensus string problem and the complexity of comparing hidden Markov models. J Comput Syst Sci 65(3):545–569CrossRef
Mackenzie A, Nickerson P (2009) The time trap: the classic book on time management. Amacom Books, New York
McCowan I, Carletta J, Kraaij W, Ashby S, Bourban S, Flynn M, Guillemot M, Hain T, Kadlec J, Karaiskos V et al (2005) The AMI meeting corpus. In: Proceedings of methods and techniques in behavioral research
Nagata M, Morimoto T (1994) First steps towards statistical modeling of dialogue to predict the speech act type of the next utterance. Speech Commun 15(3–4):193–203CrossRef
Panko RR, Kinney ST (1995) Meeting profiles: size, duration, and location. In: Proceedings of the twenty-eighth Hawaii international conference on system sciences, vol 4, pp 1002–1011
Purver M, Dowding J, Niekrasz J, Ehlen P, Noorbaloochi S, Peters S (2007) Detecting and summarizing action items in multi-party dialogue. In: Proceedings of the 8th special interest group on discourse and dialogue workshop on discourse and dialogue
Reiter S, Rigoll G (2005) Multimodal meeting analysis by segmentation and classification of meeting events based on a higher level semantic approach. In: Proceedings of international conference on acoustics, speech, and signal processing, vol 2, pp 161–164
Romano NC Jr, Nunamaker JF Jr (2001) Meeting analysis: findings from research and practice. In: Proceedings of the 34th annual Hawaii international conference on system sciences
Ron D, Singer Y, Tishby N (1996) The power of amnesia: learning probabilistic automata with variable memory length. Mach Learn 25(2–3):117–149CrossRefMATH
Scheidel TM (1967) Persuasive speaking. Scott, Foresman and Company, Glenview
Sternthal B, Phillips LW, Dholakia R (1978) The persuasive effect of scarce credibility: a situational analysis. Public Opin Q 42(3):285–314CrossRef
Stolcke A, Ries K, Coccaro N, Shriberg E, Bates R, Jurafsky D, Taylor P, Martin R, Ess-Dykema CV, Meteer M (2000) Dialogue act modeling for automatic tagging and recognition of conversational speech. Comput Linguist 26(3):339–373CrossRef
Tur G, Stolcke A, Voss L, Peters S, Hakkani-Tur D, Dowding J, Favre B, Fernandez R, Frampton M, Frandsen M, Frederickson C, Graciarena M, Kintzing D, Leveque K, Mason S, Niekrasz J, Purver M, Riedhammer K, Shriberg E, Tien J, Vergyri D, Yang F (2010) The CALO meeting assistant system. IEEE Trans Audio Speech and Lang Process 18(6):1601–1611CrossRef
van der Aalst WMP (1998) The application of petri nets to workflow management. J Circuits Syst Comput 8(01):21–66CrossRef
Metadata
Title
Learning about meetings
Authors
Been Kim
Cynthia Rudin
Publication date
01-09-2014
Publisher
Springer US
Published in
Data Mining and Knowledge Discovery / Issue 5-6/2014
Print ISSN: 1384-5810
Electronic ISSN: 1573-756X
DOI
https://doi.org/10.1007/s10618-014-0348-z

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