{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:FPSRL4I45EQZGSQAV7CL2Y6QHX","short_pith_number":"pith:FPSRL4I4","schema_version":"1.0","canonical_sha256":"2be515f11ce921934a00afc4bd63d03dfcd5a361d6a4856f40e0122e8b7eea35","source":{"kind":"arxiv","id":"2105.06804","version":2},"attestation_state":"computed","paper":{"title":"Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Shuai Zhang, Weiming Lu, Wen Wang, Xinyin Ma, Yongliang Shen, Zeqi Tan","submitted_at":"2021-05-14T12:52:34Z","abstract_excerpt":"Named entity recognition (NER) is a well-studied task in natural language processing. Traditional NER research only deals with flat entities and ignores nested entities. The span-based methods treat entity recognition as a span classification task. Although these methods have the innate ability to handle nested NER, they suffer from high computational cost, ignorance of boundary information, under-utilization of the spans that partially match with entities, and difficulties in long entity recognition. To tackle these issues, we propose a two-stage entity identifier. First we generate span 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":"2105.06804","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-05-14T12:52:34Z","cross_cats_sorted":[],"title_canon_sha256":"8e6a9ad1423d3fc9a595d06205a9e987e36940e3518462a18868036392c021bc","abstract_canon_sha256":"0e38b81d9e56e25d2ec3576c8657b6b9fab8f8d5ecde4d6262cddddb9e2edfec"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:57:21.066431Z","signature_b64":"7YwJJXCR1mixgjDQeenCdOip54zUKLpAvjgzL3CXiWb5EGI16fAIOI+KisdGNrK/LzQy48ms9kIVkmEgmriLAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2be515f11ce921934a00afc4bd63d03dfcd5a361d6a4856f40e0122e8b7eea35","last_reissued_at":"2026-07-05T02:57:21.065960Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:57:21.065960Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Shuai Zhang, Weiming Lu, Wen Wang, Xinyin Ma, Yongliang Shen, Zeqi Tan","submitted_at":"2021-05-14T12:52:34Z","abstract_excerpt":"Named entity recognition (NER) is a well-studied task in natural language processing. Traditional NER research only deals with flat entities and ignores nested entities. The span-based methods treat entity recognition as a span classification task. Although these methods have the innate ability to handle nested NER, they suffer from high computational cost, ignorance of boundary information, under-utilization of the spans that partially match with entities, and difficulties in long entity recognition. To tackle these issues, we propose a two-stage entity identifier. First we generate span prop"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.06804","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/2105.06804/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":"2105.06804","created_at":"2026-07-05T02:57:21.066014+00:00"},{"alias_kind":"arxiv_version","alias_value":"2105.06804v2","created_at":"2026-07-05T02:57:21.066014+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.06804","created_at":"2026-07-05T02:57:21.066014+00:00"},{"alias_kind":"pith_short_12","alias_value":"FPSRL4I45EQZ","created_at":"2026-07-05T02:57:21.066014+00:00"},{"alias_kind":"pith_short_16","alias_value":"FPSRL4I45EQZGSQA","created_at":"2026-07-05T02:57:21.066014+00:00"},{"alias_kind":"pith_short_8","alias_value":"FPSRL4I4","created_at":"2026-07-05T02:57:21.066014+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2409.11022","citing_title":"DynamicNER: A Dynamic, Multilingual, and Fine-Grained Dataset for LLM-based Named Entity Recognition","ref_index":43,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FPSRL4I45EQZGSQAV7CL2Y6QHX","json":"https://pith.science/pith/FPSRL4I45EQZGSQAV7CL2Y6QHX.json","graph_json":"https://pith.science/api/pith-number/FPSRL4I45EQZGSQAV7CL2Y6QHX/graph.json","events_json":"https://pith.science/api/pith-number/FPSRL4I45EQZGSQAV7CL2Y6QHX/events.json","paper":"https://pith.science/paper/FPSRL4I4"},"agent_actions":{"view_html":"https://pith.science/pith/FPSRL4I45EQZGSQAV7CL2Y6QHX","download_json":"https://pith.science/pith/FPSRL4I45EQZGSQAV7CL2Y6QHX.json","view_paper":"https://pith.science/paper/FPSRL4I4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2105.06804&json=true","fetch_graph":"https://pith.science/api/pith-number/FPSRL4I45EQZGSQAV7CL2Y6QHX/graph.json","fetch_events":"https://pith.science/api/pith-number/FPSRL4I45EQZGSQAV7CL2Y6QHX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FPSRL4I45EQZGSQAV7CL2Y6QHX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FPSRL4I45EQZGSQAV7CL2Y6QHX/action/storage_attestation","attest_author":"https://pith.science/pith/FPSRL4I45EQZGSQAV7CL2Y6QHX/action/author_attestation","sign_citation":"https://pith.science/pith/FPSRL4I45EQZGSQAV7CL2Y6QHX/action/citation_signature","submit_replication":"https://pith.science/pith/FPSRL4I45EQZGSQAV7CL2Y6QHX/action/replication_record"}},"created_at":"2026-07-05T02:57:21.066014+00:00","updated_at":"2026-07-05T02:57:21.066014+00:00"}