{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:TR7S3UYXOQILFZI3WOO6PZSVGW","short_pith_number":"pith:TR7S3UYX","schema_version":"1.0","canonical_sha256":"9c7f2dd3177410b2e51bb39de7e65535b45c68b586158a94db17aa07a8e37b6c","source":{"kind":"arxiv","id":"1903.05929","version":3},"attestation_state":"computed","paper":{"title":"Absit invidia verbo: Comparing Deep Learning methods for offensive language","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bogdan Lazarescu, Christo Lolov, Silvia Sapora","submitted_at":"2019-03-14T11:59:01Z","abstract_excerpt":"This document describes our approach to building an Offensive Language Classifier. More specifically, the OffensEval 2019 competition required us to build three classifiers with slightly different goals:\n  - Offensive language identification: would classify a tweet as offensive or not.\n  - Automatic categorization of offense types: would recognize if the target of the offense was an individual or not.\n  - Offense target identification: would identify the target of the offense between an individual, group or other.\n  In this report, we will discuss the different architectures, algorithms and pr"},"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":"1903.05929","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-03-14T11:59:01Z","cross_cats_sorted":[],"title_canon_sha256":"d8de2ce413405a0383b67c0be807ea55c45b7d43fda0e5103155d081d7dbcef7","abstract_canon_sha256":"31412ab90343f5919e8e973d2266dab718da0f9a5c7ecddfb1fa538bafaeae92"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:33.110128Z","signature_b64":"2GRSYJtJOMBeKQKEwjQI9rtc3NfZAvNNBsI04EAqPCHXgydgExlwtzpS5TUi1sEPj/S76H5xGTzSe7jUF0bDBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9c7f2dd3177410b2e51bb39de7e65535b45c68b586158a94db17aa07a8e37b6c","last_reissued_at":"2026-05-17T23:50:33.109612Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:33.109612Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Absit invidia verbo: Comparing Deep Learning methods for offensive language","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bogdan Lazarescu, Christo Lolov, Silvia Sapora","submitted_at":"2019-03-14T11:59:01Z","abstract_excerpt":"This document describes our approach to building an Offensive Language Classifier. More specifically, the OffensEval 2019 competition required us to build three classifiers with slightly different goals:\n  - Offensive language identification: would classify a tweet as offensive or not.\n  - Automatic categorization of offense types: would recognize if the target of the offense was an individual or not.\n  - Offense target identification: would identify the target of the offense between an individual, group or other.\n  In this report, we will discuss the different architectures, algorithms and pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.05929","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1903.05929","created_at":"2026-05-17T23:50:33.109701+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.05929v3","created_at":"2026-05-17T23:50:33.109701+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.05929","created_at":"2026-05-17T23:50:33.109701+00:00"},{"alias_kind":"pith_short_12","alias_value":"TR7S3UYXOQIL","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_16","alias_value":"TR7S3UYXOQILFZI3","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_8","alias_value":"TR7S3UYX","created_at":"2026-05-18T12:33:30.264802+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TR7S3UYXOQILFZI3WOO6PZSVGW","json":"https://pith.science/pith/TR7S3UYXOQILFZI3WOO6PZSVGW.json","graph_json":"https://pith.science/api/pith-number/TR7S3UYXOQILFZI3WOO6PZSVGW/graph.json","events_json":"https://pith.science/api/pith-number/TR7S3UYXOQILFZI3WOO6PZSVGW/events.json","paper":"https://pith.science/paper/TR7S3UYX"},"agent_actions":{"view_html":"https://pith.science/pith/TR7S3UYXOQILFZI3WOO6PZSVGW","download_json":"https://pith.science/pith/TR7S3UYXOQILFZI3WOO6PZSVGW.json","view_paper":"https://pith.science/paper/TR7S3UYX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.05929&json=true","fetch_graph":"https://pith.science/api/pith-number/TR7S3UYXOQILFZI3WOO6PZSVGW/graph.json","fetch_events":"https://pith.science/api/pith-number/TR7S3UYXOQILFZI3WOO6PZSVGW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TR7S3UYXOQILFZI3WOO6PZSVGW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TR7S3UYXOQILFZI3WOO6PZSVGW/action/storage_attestation","attest_author":"https://pith.science/pith/TR7S3UYXOQILFZI3WOO6PZSVGW/action/author_attestation","sign_citation":"https://pith.science/pith/TR7S3UYXOQILFZI3WOO6PZSVGW/action/citation_signature","submit_replication":"https://pith.science/pith/TR7S3UYXOQILFZI3WOO6PZSVGW/action/replication_record"}},"created_at":"2026-05-17T23:50:33.109701+00:00","updated_at":"2026-05-17T23:50:33.109701+00:00"}