Elements of a computational model for multi-party discourse: the turn-taking behavior of Supreme Court justices.
This work explores computational models of multi-party discourse, using transcripts from U.S. Supreme Court oral arguments. The turn-taking behavior of participants is treated as a supervised sequence-labeling problem and modeled using first- and second-order conditional random fields (CRFs). We spe...
| Publicado en: | Journal of the American Society for Information Science & Technology Vol. 60; no. 8; pp. 1607 - 1616 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Aug2009
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105395970&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105395970 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15322882 IGD jtl: Journal of the American Society for Information Science & Technology issn: 15322882 maglogo: Y pubinfo: dt: Aug2009 vid: 60 iid: 8 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 105395970 2010359182 10.1002/asi.21087 105395970 ppf: 1607 ppct: 9 formats: tig: atl: Elements of a computational model for multi-party discourse: the turn-taking behavior of Supreme Court justices. aug: au: Hawes T Lin J Resnik P affil: Department of Linguistics and Institute for Advanced Computer Studies, University of Maryland, Marie Mount Hall, College Park, MD 20742 sug: subj: Courts United States Natural Language Processing Speech Confidence Intervals Funding Source Linguistics United States Human ab: This work explores computational models of multi-party discourse, using transcripts from U.S. Supreme Court oral arguments. The turn-taking behavior of participants is treated as a supervised sequence-labeling problem and modeled using first- and second-order conditional random fields (CRFs). We specifically explore the hypothesis that discourse markers and personal references provide important features in such models. Results from a sequence prediction experiment demonstrate that incorporating these two types of features yields significant improvements in accuracy. Our experiments are couched in the broader context of developing tools to support legal scholarship, although we see other natural language processing applications as well. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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