{"schema":"record/v1","id":"art_01M5MTC2DA4PGDRKW44GTRKPG0","type":"article","revision":1,"etag":"\"r1.s0\"","title":"How are character networks extracted from screenplays and novels, how accurate are the steps, and has the quality of extracted networks been evaluated?","summary":"Extraction is a pipeline (identify characters, detect interactions, build the graph) and each step is assessed separately. For screenplays, Agarwal et al. tag every line (scene boundary, description, character, dialogue, metadata) and report macro-F1 0.96 for an ML ensemble vs 0.69 for a regex baseline on an unseen test set; networks from predicted tags correlate better with gold-tag networks. For novels, Elson et al. report attributing 57% of quotes with 96% accuracy (precision favoured over recall). The survey says few papers assess the extracted network itself, test data should be fiction, and filtering and static/dynamic choices change results. All numbers are the authors' reports on their data, not rerun.","status":"active","body":{"text":"Extraction is a pipeline (identify characters, detect interactions, build the graph) and each step is assessed separately. For screenplays, Agarwal et al. tag every line (scene boundary, description, character, dialogue, metadata) and report macro-F1 0.96 for an ML ensemble vs 0.69 for a regex baseline on an unseen test set; networks from predicted tags correlate better with gold-tag networks. For novels, Elson et al. report attributing 57% of quotes with 96% accuracy (precision favoured over recall). The survey says few papers assess the extracted network itself, test data should be fiction, and filtering and static/dynamic choices change results. All numbers are the authors' reports on their data, not rerun.\n\nCurator qualifications: Curator located all evidence excerpts in the work copies (automated case-insensitive prefix match). Under Amendment 11 the Labatut & Bost markers are not contradictory terms: the arXiv licence is a distribution grant to arXiv and '© acmlicensed' marks ACM's publication licence; neither grants reuse. Record: no open reuse licence; short quotes only. 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For screenplays, Agarwal et al. tag every line (scene boundary, description, character, dialogue, metadata) and report macro-F1 0.96 for an ML ensemble vs 0.69 for a regex baseline on an unseen test set; networks from predicted tags correlate better with gold-tag networks. For novels, Elson et al. report attributing 57% of quotes with 96% accuracy (precision favoured over recall). The survey says few papers assess the extracted network itself, test data should be fiction, and filtering and static/dynamic choices change results. All numbers are the authors' reports on their data, not rerun.\n\nCurator qualifications: Curator located all evidence excerpts in the work copies (automated case-insensitive prefix match). Under Amendment 11 the Labatut & Bost markers are not contradictory terms: the arXiv licence is a distribution grant to arXiv and '© acmlicensed' marks ACM's publication licence; neither grants reuse. Record: no open reuse licence; short quotes only. 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For screenplays, Agarwal et al. tag every line (scene boundary, description, character, dialogue, metadata) and report macro-F1 0.96 for an ML ensemble vs 0.69 for a regex baseline on an unseen test set; networks from predicted tags correlate better with gold-tag networks. For novels, Elson et al. report attributing 57% of quotes with 96% accuracy (precision favoured over recall). The survey says few papers assess the extracted network itself, test data should be fiction, and filtering and static/dynamic choices change results. All numbers are the authors' reports on their data, not rerun.","evidence":{"claims":[{"url":"https://aclanthology.org/W14-0907.pdf","claim":"Extraction is a pipeline (identify characters, detect interactions, build the graph) and each step is assessed separately. 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