[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122882-en":3,"doc-seo-122882-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122882,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Evaluation and Alignment of Movie Events Extracted via Machine Learning from a Narratological Perspective","The study combines distant viewing with close reading to assess how useful machine-learning event extraction is when applied to movie narratives derived from audio descriptions. The authors manually annotate events from Wikipedia summaries for three films, then align those human-identified events with events automatically extracted by ML. The evaluation focuses on coverage of movie content as well as properties such as duration, length, and event type. Results indicate computational narratology should integrate multimodal event datasets that capture both visual and verbal cues.","University of Groningen  \nEvaluation and Alignment of Movie Events Extracted via Machine Learning from a Narratological Perspective  \nZhou, Feng; Pianzola, Federico  \nPublished in:  \nProceedings of the Computational Humanities Research Conference 2023  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2023  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nZhou, F. , & Pianzola, F. (2023) . Evaluation and Alignment of Movie Events Extracted via Machine Learning from a Narratological Perspective. In A. Šeļa, F. Jannidis, & I. Romanowska (Eds.), Proceedings of the Computational Humanities Research Conference 2023 (pp. 49-62) . (CEUR Workshop Proceedings; Vol. 3558) . CEUR Workshop Proceedings ([CEUR-WS.org](CEUR-WS.org)) .  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 03-08-2026  \nEvaluation and Alignment of Movie Events Extracted via Machine Learning from a Narratological Perspective  \nFeng Zhou, Federico Pianzola∗  \nCenter for Language and Cognition, University of Groningen, Oude Kijk in ’t Jatstraat 26, 9712 EK Groningen, The Netherlands  \nAbstract  \nWe combine distant viewing and close reading to evaluate the usefulness of events extracted via machine learning from audio description of movies. To do this, we manually annotate events from Wikipedia summaries for three movies and align them to ML-extracted events. Our exploration suggests that computational narratology should combine datasets with events extracted from multimodal data sources that take into account both visual and verbal cues when detecting events.  \nKeywords  \nmovie events, narrative events, computational narratology, audio description, movie summaries  \n1. Introduction  \nThe events that compose a story are crucial for researchers conducting content analysis of movies [16] . As arti昀椀cial intelligence aims to achieve the ability to automatically understand narratives as a long-term goal [4], researchers continuously develop and improve machine learning (ML) methods for more accurate event extraction from audiovisual narratives. However, the unstructured nature of video data and advanced semantic content of movies pose challenges for computers in understanding and processing videos [17, 30] . The e昀昀ectiveness of ML in event extraction from audiovisual material still lags behind human manual extraction [16] . Moreover, narrative understanding is a border and more complex process that involves hermeneutic processes that go beyond the identi昀椀cation of events [21] . In this light, our goal is to evaluate the alignment between hu","cbCaiuVKNngsYYJZ","https://ap.wps.com/l/cbCaiuVKNngsYYJZ","pdf",1232719,1,15,"English","en",105,"# Introduction\n# Related Research","[{\"question\":\"What is the main goal of the paper?\",\"answer\":\"To evaluate how well machine-learning event extraction aligns with human interpretation of a movie’s main narrative events, focusing on usefulness for narratology.\"},{\"question\":\"How do the authors create the event datasets for evaluation?\",\"answer\":\"They manually annotate events from Wikipedia movie summaries and then manually align those events to events automatically extracted from the movies’ audio descriptions.\"},{\"question\":\"What dimensions are used to assess the event extraction and alignment?\",\"answer\":\"The evaluation emphasizes coverage of movie content and also considers properties such as duration, length, and the type of events.\"}]","Evaluation and Alignment of Movie Events Extracted via 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is the main goal of the paper?","Question",{"text":75,"@type":76},"To evaluate how well machine-learning event extraction aligns with human interpretation of a movie’s main narrative events, focusing on usefulness for narratology.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the authors create the event datasets for evaluation?",{"text":80,"@type":76},"They manually annotate events from Wikipedia movie summaries and then manually align those events to events automatically extracted from the movies’ audio descriptions.",{"name":82,"@type":73,"acceptedAnswer":83},"What dimensions are used to assess the event extraction and alignment?",{"text":84,"@type":76},"The evaluation emphasizes coverage of movie content and also considers properties such as duration, length, and the type of 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