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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.

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Classifying Email into Acts


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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

some improvements
Some Improvements
  • With more labeled data (1743 msgs), using 1g+2g+3g+4g+5g features
  • Using careful (and act specific) pre-processing of message text
  • Using act specific feature selection scheme (Info Gain, ChiSquare, etc)

- Significant performance improvements.

some examples of 4 grams
Some Examples of 4-grams

is fine with mmee

is good for mmee

i will be there

i will look for

will look for pppeople

i will see pppeople

as soon as I

$numbex per person

i will bring copies

our meeting on dday

is ok for mmee

look for pppeople

in will try to keep

-numbex i will

i will try to

i will check my

I do not have

meet at horex pm

horex pm on ddday

on ddday at horex

pppeople meet at horex

to meet at horex

would like to meet

please let mmee know

ddday at horex am

ddday at horex pm

lets plan to meet

would pppeople like to

pppeople will see pppeople

is fine with mmee

numbex-numbex pm

can pppeople meet at

ddday numbex/numbex

is good for mmee

and let mmee know

know what pppeople think

would be able to

do pppeople want to

do pppeople need to

do not want to

pppeople need to get

please let mmee know

pppeople think pppeople need

mmee know what pppeople

what do pppeople think

pppeople be able to

pppeople don not want

pppeople would be able

that would be great

Call mmee at home

Meeting (noun)

Request

Commit

Req

collective classification predicting acts from surrounding acts

Request

Request

???

Proposal

Delivery

Commit

Parent message

Child message

Collective Classification: Predicting Acts from Surrounding Acts
content versus context
Content versus Context

Request

Request

???

Proposal

Delivery

Commit

Parent message

Child message

  • Content: Bag of Words features only (using only 1g features)
  • 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)

collective classification model
Collective Classification Model

Commit

Other acts

Request

Deliver

Current Msg

Parent Message

Child Message

collective classification algorithm based on dependency networks model
Collective Classification algorithm (based on Dependency Networks Model)

New inferences are accepted only if confidence is above the Confidence Threshold. This Threshold decreases linearly with iteration, and makes the algorithm works as a temperature sensitive variation of Gibbs sampling – after iteration 50, the threshold is 50% and then a pure Gibbs sampling takes place

act by act comparative results
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.

what goes next
What goes next?
  • Extend Collective classification by using the new SpeechAct classifiers (1g-5g, feat selection)
  • Online(incremental) and semi-supervised learning – CALO focus.
  • Integration of new Speech Act package to Minorthird & Iris/Calo.
  • Role discovery – network-like features + speech act