{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:2ZFH4HJLYTSTMUYVM5BLAJ2GP6","short_pith_number":"pith:2ZFH4HJL","canonical_record":{"source":{"id":"2204.12031","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-04-26T02:04:09Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"63ecf67a4c238c90f8c089376bc85044066b44ef195263cd1f5e1a6246874185","abstract_canon_sha256":"735443b23460d337dc00c1ffa1dd3fbb7171819ac39b6dac583e50457f4cbf66"},"schema_version":"1.0"},"canonical_sha256":"d64a7e1d2bc4e53653156742b027467fb99a8b24d2cebc2875707130f53997b8","source":{"kind":"arxiv","id":"2204.12031","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.12031","created_at":"2026-07-05T04:17:43Z"},{"alias_kind":"arxiv_version","alias_value":"2204.12031v1","created_at":"2026-07-05T04:17:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.12031","created_at":"2026-07-05T04:17:43Z"},{"alias_kind":"pith_short_12","alias_value":"2ZFH4HJLYTST","created_at":"2026-07-05T04:17:43Z"},{"alias_kind":"pith_short_16","alias_value":"2ZFH4HJLYTSTMUYV","created_at":"2026-07-05T04:17:43Z"},{"alias_kind":"pith_short_8","alias_value":"2ZFH4HJL","created_at":"2026-07-05T04:17:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:2ZFH4HJLYTSTMUYVM5BLAJ2GP6","target":"record","payload":{"canonical_record":{"source":{"id":"2204.12031","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-04-26T02:04:09Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"63ecf67a4c238c90f8c089376bc85044066b44ef195263cd1f5e1a6246874185","abstract_canon_sha256":"735443b23460d337dc00c1ffa1dd3fbb7171819ac39b6dac583e50457f4cbf66"},"schema_version":"1.0"},"canonical_sha256":"d64a7e1d2bc4e53653156742b027467fb99a8b24d2cebc2875707130f53997b8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:17:43.696952Z","signature_b64":"6rhPEbHVcQNpZTv3hcNqkBt4Rzn7lhO1+wbw5iYcLbdMR9DRVuvCC+/d1D8ZhouMbql/+KmcP3YgMOZw7FZdBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d64a7e1d2bc4e53653156742b027467fb99a8b24d2cebc2875707130f53997b8","last_reissued_at":"2026-07-05T04:17:43.696434Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:17:43.696434Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2204.12031","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:17:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cjHWBWAEapxEFb7J81kHFuKn88DRP8R7wyduaj+H8DWC4R87O3JLWitbKPXT0Vi6jiaIcM7arM+uR81CKuXaCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T17:39:31.048677Z"},"content_sha256":"193c29ca8b240061d0522288ee7fae0f54f700a553da2f97597bbeda03784a35","schema_version":"1.0","event_id":"sha256:193c29ca8b240061d0522288ee7fae0f54f700a553da2f97597bbeda03784a35"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:2ZFH4HJLYTSTMUYVM5BLAJ2GP6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Boundary Smoothing for Named Entity Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Enwei Zhu, Jinpeng Li","submitted_at":"2022-04-26T02:04:09Z","abstract_excerpt":"Neural named entity recognition (NER) models may easily encounter the over-confidence issue, which degrades the performance and calibration. Inspired by label smoothing and driven by the ambiguity of boundary annotation in NER engineering, we propose boundary smoothing as a regularization technique for span-based neural NER models. It re-assigns entity probabilities from annotated spans to the surrounding ones. Built on a simple but strong baseline, our model achieves results better than or competitive with previous state-of-the-art systems on eight well-known NER benchmarks. Further empirical"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.12031","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/2204.12031/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:17:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VuD9NIOEyRqN1MfVQC4AGsNyDDBAP6vFqoG6uBWyrF7ugu/dRCoUd76qzvabIj5+UI25LzWn56mw+ZPHkqcrDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T17:39:31.049349Z"},"content_sha256":"cb1948bfff5655fa3cac459a1ab3c2a355db5b60cd355a119f6f1c6d27fa3348","schema_version":"1.0","event_id":"sha256:cb1948bfff5655fa3cac459a1ab3c2a355db5b60cd355a119f6f1c6d27fa3348"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2ZFH4HJLYTSTMUYVM5BLAJ2GP6/bundle.json","state_url":"https://pith.science/pith/2ZFH4HJLYTSTMUYVM5BLAJ2GP6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2ZFH4HJLYTSTMUYVM5BLAJ2GP6/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-09T17:39:31Z","links":{"resolver":"https://pith.science/pith/2ZFH4HJLYTSTMUYVM5BLAJ2GP6","bundle":"https://pith.science/pith/2ZFH4HJLYTSTMUYVM5BLAJ2GP6/bundle.json","state":"https://pith.science/pith/2ZFH4HJLYTSTMUYVM5BLAJ2GP6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2ZFH4HJLYTSTMUYVM5BLAJ2GP6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:2ZFH4HJLYTSTMUYVM5BLAJ2GP6","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"735443b23460d337dc00c1ffa1dd3fbb7171819ac39b6dac583e50457f4cbf66","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-04-26T02:04:09Z","title_canon_sha256":"63ecf67a4c238c90f8c089376bc85044066b44ef195263cd1f5e1a6246874185"},"schema_version":"1.0","source":{"id":"2204.12031","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.12031","created_at":"2026-07-05T04:17:43Z"},{"alias_kind":"arxiv_version","alias_value":"2204.12031v1","created_at":"2026-07-05T04:17:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.12031","created_at":"2026-07-05T04:17:43Z"},{"alias_kind":"pith_short_12","alias_value":"2ZFH4HJLYTST","created_at":"2026-07-05T04:17:43Z"},{"alias_kind":"pith_short_16","alias_value":"2ZFH4HJLYTSTMUYV","created_at":"2026-07-05T04:17:43Z"},{"alias_kind":"pith_short_8","alias_value":"2ZFH4HJL","created_at":"2026-07-05T04:17:43Z"}],"graph_snapshots":[{"event_id":"sha256:cb1948bfff5655fa3cac459a1ab3c2a355db5b60cd355a119f6f1c6d27fa3348","target":"graph","created_at":"2026-07-05T04:17:43Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2204.12031/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Neural named entity recognition (NER) models may easily encounter the over-confidence issue, which degrades the performance and calibration. Inspired by label smoothing and driven by the ambiguity of boundary annotation in NER engineering, we propose boundary smoothing as a regularization technique for span-based neural NER models. It re-assigns entity probabilities from annotated spans to the surrounding ones. Built on a simple but strong baseline, our model achieves results better than or competitive with previous state-of-the-art systems on eight well-known NER benchmarks. Further empirical","authors_text":"Enwei Zhu, Jinpeng Li","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-04-26T02:04:09Z","title":"Boundary Smoothing for Named Entity Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.12031","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:193c29ca8b240061d0522288ee7fae0f54f700a553da2f97597bbeda03784a35","target":"record","created_at":"2026-07-05T04:17:43Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"735443b23460d337dc00c1ffa1dd3fbb7171819ac39b6dac583e50457f4cbf66","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-04-26T02:04:09Z","title_canon_sha256":"63ecf67a4c238c90f8c089376bc85044066b44ef195263cd1f5e1a6246874185"},"schema_version":"1.0","source":{"id":"2204.12031","kind":"arxiv","version":1}},"canonical_sha256":"d64a7e1d2bc4e53653156742b027467fb99a8b24d2cebc2875707130f53997b8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d64a7e1d2bc4e53653156742b027467fb99a8b24d2cebc2875707130f53997b8","first_computed_at":"2026-07-05T04:17:43.696434Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:17:43.696434Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6rhPEbHVcQNpZTv3hcNqkBt4Rzn7lhO1+wbw5iYcLbdMR9DRVuvCC+/d1D8ZhouMbql/+KmcP3YgMOZw7FZdBg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:17:43.696952Z","signed_message":"canonical_sha256_bytes"},"source_id":"2204.12031","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:193c29ca8b240061d0522288ee7fae0f54f700a553da2f97597bbeda03784a35","sha256:cb1948bfff5655fa3cac459a1ab3c2a355db5b60cd355a119f6f1c6d27fa3348"],"state_sha256":"672c222e44c104d1ce349f4e24870158c6c4bb611aeb5c8c0647ffb0e7349286"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mw0plom/SKTsu3DbYHfDC1UIdlmbCzvCzh15AD/jNXPADqO5oxiyyojgLR5t7ywoXlLYFVEkdkonA2BCzFeABg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T17:39:31.055049Z","bundle_sha256":"13b71fa7bfeeb6de85fc8a343ab9c325b434d6f754e43e840a33806655042061"}}