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Learning TFC Meeting, SRI March 2005 On the Collective Classification of Email “Speech Acts” PowerPoint Presentation
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Learning TFC Meeting, SRI March 2005 On the Collective Classification of Email “Speech Acts”

Learning TFC Meeting, SRI March 2005 On the Collective Classification of Email “Speech Acts”

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Learning TFC Meeting, SRI March 2005 On the Collective Classification of Email “Speech Acts”

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  1. Learning TFC Meeting, SRI March 2005On the Collective Classification of Email “Speech Acts” Vitor R. Carvalho & William W. Cohen Carnegie Mellon University

  2. Classifying Email into Acts • From EMNLP-04, Learning to Classify Email into Speech Acts, Cohen-Carvalho-Mitchell • An Act is described as a verb-noun pair (e.g., propose meeting, request information) - Not all pairs make sense. One single email message may contain multiple acts. • Try to describe commonly observed behaviors, rather than all possible speech acts in English. Also include non-linguistic usage of email (e.g. delivery of files) Verbs Nouns

  3. Idea: Predicting Acts from Surrounding Acts Example of Email Sequence • Strong correlation with previous and next message’s acts Delivery Request Request Proposal Delivery Commit Commit Delivery • Act has little or no correlation with other acts of same message <<In-ReplyTo>> Commit

  4. Related work on the Sequential Nature of Negotiations • Winograd and Flores, 1986:“Conversation for Action Structure” • Murakoshi et al. 1999; “Construction of Deliberation Structure in Email”

  5. Data: CSPACE Corpus • Few large, free, natural email corpora are available • CSPACE corpus (Kraut & Fussell) • Emails associated with a semester-long project for Carnegie Mellon MBA students in 1997 • 15,000 messages from 277 students, divided in 50 teams (4 to 6 students/team) • Rich in task negotiation. • More than 1500 messages (from 4 teams) were labeled in terms of “Speech Act”. • One of the teams was double labeled, and the inter-annotator agreement ranges from 72 to 83% (Kappa) for the most frequent acts.

  6. Evidence of Sequential Correlation of Acts • Transition diagram for most common verbs from CSPACE corpus • It is NOT a Probabilistic DFA • Act sequence patterns: (Request, Deliver+), (Propose, Commit+, Deliver+), (Propose, Deliver+), most common act was Deliver • Less regularity than the expected ( considering previous deterministic negotiation state diagrams)

  7. Content versus Context Request Request ??? Proposal Delivery Commit Parent message Child message • Content: Bag of Words features only • Context:Parent and Child Features only ( table below) • 8 MaxEnt classifiers, trained on 3F2 and tested on 1F3 team dataset • Only 1st child message was considered (vast majority – more than 95%) Kappa Values on 1F3 using Relational (Context) features and Textual (Content) features. Set of Context Features (Relational)

  8. Dependency networks are probabilistic graphical models in which the full joint distribution of the network is approximated with a set of conditional distributions that can be learned independently. The conditional probability distributions in a DN are calculated for each node given its neighboring nodes (its Markov blanket). Collective Classification using Dependency Networks • No acyclicity constraint. Simple parameter estimation – approximate inference (Gibbs sampling) • In this case, Markov blanket = parent message and child message • Heckerman et al., JMLR-2000. Neville & Jensen, KDD-MRDM-2003.

  9. Collective Classification algorithm (based on Dependency Networks Model)

  10. Agreement versus Iteration • Kappa versus iteration on 1F3 team dataset, using classifiers trained on 3F2 team data.

  11. Leave-one-team-out Experiments Kappa Values • 4 teams: 1f3(170 msgs), 2f2(137 msgs), 3f2(249 msgs) and 4f4(165 msgs) • (x axis)= Bag-of-words only • (y-axis) = Collective classification results • Different teams present different styles for negotiations and task delegation.

  12. Leave-one-team-out Experiments • Consistent improvement of Commissive, Commit and Meet acts Kappa Values

  13. Leave-one-team-out Experiments • Deliver and dData performance usually decreases • Associated with data distribution, FYI, file sharing, etc. • For “non-delivery”, improvement in avg. Kappa is statistically significant (p=0.01 on a two-tailed T-test) Kappa Values

  14. Act by Act Comparative Results Kappa values with and without collective classification, averaged over the four test sets in the leave-one-team out experiment.

  15. Discussion and Conclusion • Sequential patterns of email acts were observed in the CSPACE corpus. • These patterns, when studied an artificial experiment, were shown to contain valuable information to the email-act classification problem. • Different teams present different styles for negotiations and task delegation. • We proposed a collective classification scheme for Email Speech Acts of messages. (based on a Dependency Network model)

  16. Conclusion • Modest improvements over the baseline (bag of words) were observed on acts related to negotiation (Request, Commit, Propose, Meet, etc) . A performance deterioration was observed for Delivery/dData (acts less associated with negotiations) • Agrees with general intuition on the sequential nature of negotiation steps. • Degree of linkage in our dataset is small – which makes the observed results encouraging.