Recovery and Reclamation: A Continuing Project

Calo, Mary Ann

doi:10.1086/444676

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The Ymeeting Scenario Data1

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“…Our research work is part of the Cognitive Assistant that Learns and Organizes project (CALO, 2003). A goal of this project is to create an artificial assistant that can understand meetings and use this understanding to assist meeting participants during and after meetings.…”

Section: The Ymeeting Scenario Datamentioning

confidence: 99%

You are what you say

Banerjee

Rudnicky

2006

Proceedings of the HLT-NAACL 2006 Workshop on Analyzing Conversations in Text and Speech - ACTS '09

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Our goal is to automatically detect the functional roles that meeting participants play, as well as the expertise they bring to meetings. To perform this task, we build decision tree classifiers that use a combination of simple speech features (speech lengths and spoken keywords) extracted from the participants' speech in meetings. We show that this algorithm results in a role detection accuracy of 83% on unseen test data, where the random baseline is 33.3%. We also introduce a simple aggregation mechanism that combines evidence of the participants' expertise from multiple meetings. We show that this aggregation mechanism improves the role detection accuracy from 66.7% (when aggregating over a single meeting) to 83% (when aggregating over 5 meetings).

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Section: The Ymeeting Scenario Datamentioning

confidence: 99%