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Lights, Camera, Action: Knowledge Extraction from Movie Scripts

Published: 18 May 2015 Publication History

Abstract

With the success of large knowledge graphs, research on automatically acquiring commonsense knowledge is revived. One kind of knowledge that has not received attention is that of human activities. This paper presents an information extraction pipeline for systematically distilling activity knowledge from a corpus of movie scripts. Our semantic frames capture activities together with their participating agents and their typical spatial, temporal and sequential contexts. The resulting knowledge base comprises about 250,000 activities with links to specific movie scenes where they occur.

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Cited By

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  • (2024)Automated Adaptive Cinematography for User Interaction in Open WorldIEEE Transactions on Multimedia10.1109/TMM.2023.334709226(6178-6190)Online publication date: 2024
  • (2024)Automatic cinematography for body movement involved virtual communicationIET Communications10.1049/cmu2.12748Online publication date: 20-Mar-2024
  • (2023)Adaptive Auto-Cinematography in Open Worlds2023 IEEE 6th International Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR59079.2023.00025(1-6)Online publication date: Aug-2023
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  1. Lights, Camera, Action: Knowledge Extraction from Movie Scripts

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    cover image ACM Other conferences
    WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web
    May 2015
    1602 pages
    ISBN:9781450334730
    DOI:10.1145/2740908
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    • IW3C2: International World Wide Web Conference Committee

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 18 May 2015

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    Author Tags

    1. activity knowledge
    2. commonsense knowledge acquisition

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    WWW '15
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    • IW3C2

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    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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    Cited By

    View all
    • (2024)Automated Adaptive Cinematography for User Interaction in Open WorldIEEE Transactions on Multimedia10.1109/TMM.2023.334709226(6178-6190)Online publication date: 2024
    • (2024)Automatic cinematography for body movement involved virtual communicationIET Communications10.1049/cmu2.12748Online publication date: 20-Mar-2024
    • (2023)Adaptive Auto-Cinematography in Open Worlds2023 IEEE 6th International Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR59079.2023.00025(1-6)Online publication date: Aug-2023
    • (2020)The B-Subtle framework: tailoring subtitles to your needsLanguage Resources and Evaluation10.1007/s10579-020-09507-3Online publication date: 11-Oct-2020
    • (2017)SLANT+: A Nonlinear Model for Opinion Dynamics in Social Networks2017 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM.2017.117(931-936)Online publication date: Nov-2017
    • (2016)WebBrain: Joint Neural Learning of Large-Scale Commonsense KnowledgeThe Semantic Web – ISWC 201610.1007/978-3-319-46523-4_7(102-118)Online publication date: 23-Sep-2016
    • (2015)Spatial role labeling with convolutional neural networksProceedings of the 9th Workshop on Geographic Information Retrieval10.1145/2837689.2837706(1-7)Online publication date: 26-Nov-2015

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