[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121859-en":3,"doc-seo-121859-105":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":20,"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},121859,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Evaluation and Alignment of Movie Events Extracted via Machine Learning from a Narratological Perspective","The study evaluates how useful machine-learning (ML) extracted movie events are for narratological analysis by combining distant viewing with close reading. Events are manually annotated from Wikipedia summaries for three movies and then aligned to events extracted automatically from audio descriptions. The findings indicate that computational narratology benefits from multimodal event datasets that jointly capture both visual and verbal cues, improving the detection of narrative structure beyond purely automated extraction.","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.)  \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 human interpretation of the ma","cbCaijSadJ9lMVc1","https://ap.wps.com/l/cbCaijSadJ9lMVc1","pdf",1201385,1,15,"English","en",105,"# Introduction\n## Related Research\n## Methods\n## Evaluation and Alignment\n## Discussion\n## Conclusion","[{\"question\":\"How does the study evaluate the usefulness of ML-extracted movie events?\",\"answer\":\"It combines distant viewing and close reading: manually annotated events from Wikipedia summaries are aligned to ML-extracted events from audio descriptions to assess coverage and alignment.\"},{\"question\":\"What data sources are used for event annotation and extraction?\",\"answer\":\"The study uses Wikipedia movie summaries for manual annotation and audio descriptions for ML-based event extraction, focusing on how well the events correspond across sources.\"},{\"question\":\"What do the results suggest for computational narratology?\",\"answer\":\"Computational narratology should rely on multimodal datasets where event detection accounts for both visual and verbal cues, rather than using unimodal extraction alone.\"}]","Evaluation and Alignment of Movie Events Extracted via Machine Learning from a Narratological Perspective | 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does the study evaluate the usefulness of ML-extracted movie events?","Question",{"text":75,"@type":76},"It combines distant viewing and close reading: manually annotated events from Wikipedia summaries are aligned to ML-extracted events from audio descriptions to assess coverage and alignment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data sources are used for event annotation and extraction?",{"text":80,"@type":76},"The study uses Wikipedia movie summaries for manual annotation and audio descriptions for ML-based event extraction, focusing on how well the events correspond across sources.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the results suggest for computational narratology?",{"text":84,"@type":76},"Computational narratology should rely on multimodal datasets where event detection accounts for both visual and verbal cues, rather than using unimodal extraction 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