{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:H6SYLBHC5ALLFDNGSGOY4WYMZK","short_pith_number":"pith:H6SYLBHC","schema_version":"1.0","canonical_sha256":"3fa58584e2e816b28da6919d8e5b0ccaa60191c105ef8976df7f63071258e9c2","source":{"kind":"arxiv","id":"2112.08033","version":1},"attestation_state":"computed","paper":{"title":"Named entity recognition architecture combining contextual and global features","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Antoine Doucet, Jose G. Moreno, Nicolas Sidere, Senja Pollak, Tran Thi Hong Hanh","submitted_at":"2021-12-15T10:54:36Z","abstract_excerpt":"Named entity recognition (NER) is an information extraction technique that aims to locate and classify named entities (e.g., organizations, locations,...) within a document into predefined categories. Correctly identifying these phrases plays a significant role in simplifying information access. However, it remains a difficult task because named entities (NEs) have multiple forms and they are context-dependent. While the context can be represented by contextual features, global relations are often misrepresented by those models. In this paper, we propose the combination of contextual features "},"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":"2112.08033","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-12-15T10:54:36Z","cross_cats_sorted":[],"title_canon_sha256":"fbad614593d5ac71278c4d23768e12028dacc57d316105e07017f5b48ef7a934","abstract_canon_sha256":"40c2611fbcd7518a840204b3af41917218cc77ca75d5c25b79ed68f07a967e64"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:41:09.944307Z","signature_b64":"8IkCPwlw2y8vMc4Hu3cNkHWQ4SeFny5TdFo37sGgI9biqjX53OrLLvOCAqJCCObyXz/HQkJhDWxkESrPOrqwBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3fa58584e2e816b28da6919d8e5b0ccaa60191c105ef8976df7f63071258e9c2","last_reissued_at":"2026-07-05T03:41:09.943969Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:41:09.943969Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Named entity recognition architecture combining contextual and global features","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Antoine Doucet, Jose G. Moreno, Nicolas Sidere, Senja Pollak, Tran Thi Hong Hanh","submitted_at":"2021-12-15T10:54:36Z","abstract_excerpt":"Named entity recognition (NER) is an information extraction technique that aims to locate and classify named entities (e.g., organizations, locations,...) within a document into predefined categories. Correctly identifying these phrases plays a significant role in simplifying information access. However, it remains a difficult task because named entities (NEs) have multiple forms and they are context-dependent. While the context can be represented by contextual features, global relations are often misrepresented by those models. In this paper, we propose the combination of contextual features "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.08033","kind":"arxiv","version":1},"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/2112.08033/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":"2112.08033","created_at":"2026-07-05T03:41:09.944024+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.08033v1","created_at":"2026-07-05T03:41:09.944024+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.08033","created_at":"2026-07-05T03:41:09.944024+00:00"},{"alias_kind":"pith_short_12","alias_value":"H6SYLBHC5ALL","created_at":"2026-07-05T03:41:09.944024+00:00"},{"alias_kind":"pith_short_16","alias_value":"H6SYLBHC5ALLFDNG","created_at":"2026-07-05T03:41:09.944024+00:00"},{"alias_kind":"pith_short_8","alias_value":"H6SYLBHC","created_at":"2026-07-05T03:41:09.944024+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/H6SYLBHC5ALLFDNGSGOY4WYMZK","json":"https://pith.science/pith/H6SYLBHC5ALLFDNGSGOY4WYMZK.json","graph_json":"https://pith.science/api/pith-number/H6SYLBHC5ALLFDNGSGOY4WYMZK/graph.json","events_json":"https://pith.science/api/pith-number/H6SYLBHC5ALLFDNGSGOY4WYMZK/events.json","paper":"https://pith.science/paper/H6SYLBHC"},"agent_actions":{"view_html":"https://pith.science/pith/H6SYLBHC5ALLFDNGSGOY4WYMZK","download_json":"https://pith.science/pith/H6SYLBHC5ALLFDNGSGOY4WYMZK.json","view_paper":"https://pith.science/paper/H6SYLBHC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.08033&json=true","fetch_graph":"https://pith.science/api/pith-number/H6SYLBHC5ALLFDNGSGOY4WYMZK/graph.json","fetch_events":"https://pith.science/api/pith-number/H6SYLBHC5ALLFDNGSGOY4WYMZK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H6SYLBHC5ALLFDNGSGOY4WYMZK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H6SYLBHC5ALLFDNGSGOY4WYMZK/action/storage_attestation","attest_author":"https://pith.science/pith/H6SYLBHC5ALLFDNGSGOY4WYMZK/action/author_attestation","sign_citation":"https://pith.science/pith/H6SYLBHC5ALLFDNGSGOY4WYMZK/action/citation_signature","submit_replication":"https://pith.science/pith/H6SYLBHC5ALLFDNGSGOY4WYMZK/action/replication_record"}},"created_at":"2026-07-05T03:41:09.944024+00:00","updated_at":"2026-07-05T03:41:09.944024+00:00"}