Get out the vote determining support or opposition from congressional floor debate transcripts
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Get out the vote: Determining support or opposition from Congressional floor- debate transcripts. Matt Thomas, Bo Pang, and Lillian Lee Cornell University. Get Out the Vote. * http://www.cs.cornell.edu/home/llee/papers/tpl-convote.home.html. Motivation. Congressional debates contains

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Get out the vote determining support or opposition from congressional floor debate transcripts

Get out the vote: Determining support or opposition from Congressional floor-debate transcripts

Matt Thomas, Bo Pang, and Lillian Lee Cornell University


Get out the vote

Get Out the Vote

* http://www.cs.cornell.edu/home/llee/papers/tpl-convote.home.html


Motivation

Motivation

  • Congressional debates contains

    • Very rich Language

    • Wide variety of topics

    • More time spent on evidence

  • Agreement information between debaters can provide additional benefits for documents that are relatively harder to classify individually.


Corpus

Corpus

  • GovTrack

  • House of Representatives’ floor-debates transcripts for 2005

  • Concentrated on debates regarding “controversial” bills (ones in which the losing side generated at least 20% of the speeches)

  • Made sure that the speech segments from an individual debate appears in the same set


Method

Method

2 Classifiers (SVMlight)

  • Individual-Document Classifier: scores each speech-segment s in isolation.

  • Agreement Classifier: for pairs of speech segments.

    • Trained to score by-name references as how much they indicate agreement.

    • e.g “I blieve Mr. Smith’s argument is persuasive”


Method1

Method

Individual-Document Classifier:

  • Plain unigrams as features

  • Normalized presence-of-features rather than frequency-of-features.

  • Y for yea, N for nay

  • d(s): the signed distance between sand the trained SVM decision plane

  • where σs is the standard deviation of d(s) over all speech segments s in the debate in question

  • ind(s,N) = 1−ind(s,Y)


Method2

Method

Two types of Agreements:

  • Same-speaker: (A number of comments - Opinions may change)

    • Under the assumption that most speakers do not change their positions. There are two possible solutions:

      • All comments by the same speaker receive the same label Y or N .

      • Concatenation of same-speaker speech segments.

  • Different-Speaker:

    • References are:

      • Indentified by-name mention.

      • Represented as word-presence vectors derived from windows of text surrounding the reference.

      • Annotated with a positive or negative label based on the speakers’ agreements in voting.


  • Method3

    Method

    Agreement Classifier:

    • d(r) is the distance from ragreement-vectorto the SVM decision plane

    • σris the standard deviation of d(r) over all reference segments r in the debate in question.

    • αisa free parameter to specify the relative importance of the agrscores derived via tuning on the development set.

    • θagris the threshold to control the precision of the agreement links.

    Agreement-classifier precision


    Method4

    Method

    • Classification framework:

      Optimization problem: find a classification C that minimizes:


    Evaluation

    Evaluation

    Amendment/No amendment agreement Classification:

    Note that amendment-related speech segments were never included in the development or test set, since their labels are probably noisy


    Evaluation1

    Evaluation

    • Speaker-based speech-segment Classification:

    Segment-based speech-segment Classification:


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