{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:TLJZEDF6X3NTLWCJTR2YEZUU46","short_pith_number":"pith:TLJZEDF6","schema_version":"1.0","canonical_sha256":"9ad3920cbebedb35d8499c75826694e7aa15d14a64653a2d071fde90d041d92b","source":{"kind":"arxiv","id":"2109.09276","version":2},"attestation_state":"computed","paper":{"title":"From None to Severe: Predicting Severity in Movie Scripts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Fabio Gonzalez, Mahsa Shafaei, Thamar Solorio, Yigeng Zhang","submitted_at":"2021-09-20T03:01:46Z","abstract_excerpt":"In this paper, we introduce the task of predicting severity of age-restricted aspects of movie content based solely on the dialogue script. We first investigate categorizing the ordinal severity of movies on 5 aspects: Sex, Violence, Profanity, Substance consumption, and Frightening scenes. The problem is handled using a siamese network-based multitask framework which concurrently improves the interpretability of the predictions. The experimental results show that our method outperforms the previous state-of-the-art model and provides useful information to interpret model predictions. The prop"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2109.09276","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-09-20T03:01:46Z","cross_cats_sorted":[],"title_canon_sha256":"1b4399419bfbecd419498de188b7f37ad0f3d9736c9187836a3e9bd2a718a1e9","abstract_canon_sha256":"8eee640f484127b4414a4c7ea12119dc97bbc3f91757cf74f5b6e551a124ac40"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:19:46.125206Z","signature_b64":"3Jtm0yB+O0lpwlmTYMPR2EhPPORA2ZPqwtXNufXS6cvM0OM5yE3Q6eGsjfwIFJf+jYtfxcq3zjAHy3HIh4ZfBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9ad3920cbebedb35d8499c75826694e7aa15d14a64653a2d071fde90d041d92b","last_reissued_at":"2026-07-05T03:19:46.124772Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:19:46.124772Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From None to Severe: Predicting Severity in Movie Scripts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Fabio Gonzalez, Mahsa Shafaei, Thamar Solorio, Yigeng Zhang","submitted_at":"2021-09-20T03:01:46Z","abstract_excerpt":"In this paper, we introduce the task of predicting severity of age-restricted aspects of movie content based solely on the dialogue script. We first investigate categorizing the ordinal severity of movies on 5 aspects: Sex, Violence, Profanity, Substance consumption, and Frightening scenes. The problem is handled using a siamese network-based multitask framework which concurrently improves the interpretability of the predictions. The experimental results show that our method outperforms the previous state-of-the-art model and provides useful information to interpret model predictions. The prop"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.09276","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2109.09276/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2109.09276","created_at":"2026-07-05T03:19:46.124828+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.09276v2","created_at":"2026-07-05T03:19:46.124828+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.09276","created_at":"2026-07-05T03:19:46.124828+00:00"},{"alias_kind":"pith_short_12","alias_value":"TLJZEDF6X3NT","created_at":"2026-07-05T03:19:46.124828+00:00"},{"alias_kind":"pith_short_16","alias_value":"TLJZEDF6X3NTLWCJ","created_at":"2026-07-05T03:19:46.124828+00:00"},{"alias_kind":"pith_short_8","alias_value":"TLJZEDF6","created_at":"2026-07-05T03:19:46.124828+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.21181","citing_title":"FUTURE: Flexible Unlearning for Tree Ensemble","ref_index":31,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TLJZEDF6X3NTLWCJTR2YEZUU46","json":"https://pith.science/pith/TLJZEDF6X3NTLWCJTR2YEZUU46.json","graph_json":"https://pith.science/api/pith-number/TLJZEDF6X3NTLWCJTR2YEZUU46/graph.json","events_json":"https://pith.science/api/pith-number/TLJZEDF6X3NTLWCJTR2YEZUU46/events.json","paper":"https://pith.science/paper/TLJZEDF6"},"agent_actions":{"view_html":"https://pith.science/pith/TLJZEDF6X3NTLWCJTR2YEZUU46","download_json":"https://pith.science/pith/TLJZEDF6X3NTLWCJTR2YEZUU46.json","view_paper":"https://pith.science/paper/TLJZEDF6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.09276&json=true","fetch_graph":"https://pith.science/api/pith-number/TLJZEDF6X3NTLWCJTR2YEZUU46/graph.json","fetch_events":"https://pith.science/api/pith-number/TLJZEDF6X3NTLWCJTR2YEZUU46/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TLJZEDF6X3NTLWCJTR2YEZUU46/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TLJZEDF6X3NTLWCJTR2YEZUU46/action/storage_attestation","attest_author":"https://pith.science/pith/TLJZEDF6X3NTLWCJTR2YEZUU46/action/author_attestation","sign_citation":"https://pith.science/pith/TLJZEDF6X3NTLWCJTR2YEZUU46/action/citation_signature","submit_replication":"https://pith.science/pith/TLJZEDF6X3NTLWCJTR2YEZUU46/action/replication_record"}},"created_at":"2026-07-05T03:19:46.124828+00:00","updated_at":"2026-07-05T03:19:46.124828+00:00"}