{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:GLPVTNQCRIQSENJCJ4LWPDBEMA","short_pith_number":"pith:GLPVTNQC","canonical_record":{"source":{"id":"2310.17238","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-26T08:36:39Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"586e5487b3a6927543259c2dd5ac950e1af3533a7a75737a7e71053026bd0e8e","abstract_canon_sha256":"9d5d9ff539860ef70abd1fc4e96e9ac1bdf6405f3daee51228dc7b41cb07e25d"},"schema_version":"1.0"},"canonical_sha256":"32df59b6028a212235224f17678c246023b5854338ac154effbb60131af467d8","source":{"kind":"arxiv","id":"2310.17238","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.17238","created_at":"2026-07-05T07:05:21Z"},{"alias_kind":"arxiv_version","alias_value":"2310.17238v1","created_at":"2026-07-05T07:05:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.17238","created_at":"2026-07-05T07:05:21Z"},{"alias_kind":"pith_short_12","alias_value":"GLPVTNQCRIQS","created_at":"2026-07-05T07:05:21Z"},{"alias_kind":"pith_short_16","alias_value":"GLPVTNQCRIQSENJC","created_at":"2026-07-05T07:05:21Z"},{"alias_kind":"pith_short_8","alias_value":"GLPVTNQC","created_at":"2026-07-05T07:05:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:GLPVTNQCRIQSENJCJ4LWPDBEMA","target":"record","payload":{"canonical_record":{"source":{"id":"2310.17238","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-26T08:36:39Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"586e5487b3a6927543259c2dd5ac950e1af3533a7a75737a7e71053026bd0e8e","abstract_canon_sha256":"9d5d9ff539860ef70abd1fc4e96e9ac1bdf6405f3daee51228dc7b41cb07e25d"},"schema_version":"1.0"},"canonical_sha256":"32df59b6028a212235224f17678c246023b5854338ac154effbb60131af467d8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:05:21.927779Z","signature_b64":"ttJBgMvvpQxzfy1XLHDlAdj84gibiIA9TJeFETnFg91n/DQ3zWtP+sBW+JiqP4ooh+8jdodGs7npEUhUEm+6Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"32df59b6028a212235224f17678c246023b5854338ac154effbb60131af467d8","last_reissued_at":"2026-07-05T07:05:21.926910Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:05:21.926910Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.17238","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":"2026-07-05T07:05:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Cr3Gu4lDwuyoDeddrM2ik6UAbwTRyWgociCZWxFZbBc0QD99QiAnAp/iPrOKC8qKFyHnsiccs0lLXBRrYCx0AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T00:55:34.718236Z"},"content_sha256":"a56976356eb5f2b507271f4d1088269e5959ed6d7839df674621893dedd0e623","schema_version":"1.0","event_id":"sha256:a56976356eb5f2b507271f4d1088269e5959ed6d7839df674621893dedd0e623"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:GLPVTNQCRIQSENJCJ4LWPDBEMA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Kewei Tu, Songlin Yang, Wei Liu, Zhaohui Yan","submitted_at":"2023-10-26T08:36:39Z","abstract_excerpt":"Entity and Relation Extraction (ERE) is an important task in information extraction. Recent marker-based pipeline models achieve state-of-the-art performance, but still suffer from the error propagation issue. Also, most of current ERE models do not take into account higher-order interactions between multiple entities and relations, while higher-order modeling could be beneficial.In this work, we propose HyperGraph neural network for ERE ($\\hgnn{}$), which is built upon the PL-marker (a state-of-the-art marker-based pipleline model). To alleviate error propagation,we use a high-recall pruner m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.17238","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/2310.17238/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":"2026-07-05T07:05:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jLj3goPJjIVSTWY35ZzDn9Jdk+mX2KpYjsLQxDp1Eu+MwIwOJoq9GnsRWs5D0JZqhtHJ6R6v3Kegtg4d9UO5Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T00:55:34.718645Z"},"content_sha256":"ed046ec15d1377e91539e77742db436258f8277a70fd590d26139ef38618c9f9","schema_version":"1.0","event_id":"sha256:ed046ec15d1377e91539e77742db436258f8277a70fd590d26139ef38618c9f9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GLPVTNQCRIQSENJCJ4LWPDBEMA/bundle.json","state_url":"https://pith.science/pith/GLPVTNQCRIQSENJCJ4LWPDBEMA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GLPVTNQCRIQSENJCJ4LWPDBEMA/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":"2026-07-10T00:55:34Z","links":{"resolver":"https://pith.science/pith/GLPVTNQCRIQSENJCJ4LWPDBEMA","bundle":"https://pith.science/pith/GLPVTNQCRIQSENJCJ4LWPDBEMA/bundle.json","state":"https://pith.science/pith/GLPVTNQCRIQSENJCJ4LWPDBEMA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GLPVTNQCRIQSENJCJ4LWPDBEMA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:GLPVTNQCRIQSENJCJ4LWPDBEMA","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":"9d5d9ff539860ef70abd1fc4e96e9ac1bdf6405f3daee51228dc7b41cb07e25d","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-26T08:36:39Z","title_canon_sha256":"586e5487b3a6927543259c2dd5ac950e1af3533a7a75737a7e71053026bd0e8e"},"schema_version":"1.0","source":{"id":"2310.17238","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.17238","created_at":"2026-07-05T07:05:21Z"},{"alias_kind":"arxiv_version","alias_value":"2310.17238v1","created_at":"2026-07-05T07:05:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.17238","created_at":"2026-07-05T07:05:21Z"},{"alias_kind":"pith_short_12","alias_value":"GLPVTNQCRIQS","created_at":"2026-07-05T07:05:21Z"},{"alias_kind":"pith_short_16","alias_value":"GLPVTNQCRIQSENJC","created_at":"2026-07-05T07:05:21Z"},{"alias_kind":"pith_short_8","alias_value":"GLPVTNQC","created_at":"2026-07-05T07:05:21Z"}],"graph_snapshots":[{"event_id":"sha256:ed046ec15d1377e91539e77742db436258f8277a70fd590d26139ef38618c9f9","target":"graph","created_at":"2026-07-05T07:05:21Z","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/2310.17238/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Entity and Relation Extraction (ERE) is an important task in information extraction. Recent marker-based pipeline models achieve state-of-the-art performance, but still suffer from the error propagation issue. Also, most of current ERE models do not take into account higher-order interactions between multiple entities and relations, while higher-order modeling could be beneficial.In this work, we propose HyperGraph neural network for ERE ($\\hgnn{}$), which is built upon the PL-marker (a state-of-the-art marker-based pipleline model). To alleviate error propagation,we use a high-recall pruner m","authors_text":"Kewei Tu, Songlin Yang, Wei Liu, Zhaohui Yan","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-26T08:36:39Z","title":"Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.17238","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:a56976356eb5f2b507271f4d1088269e5959ed6d7839df674621893dedd0e623","target":"record","created_at":"2026-07-05T07:05:21Z","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":"9d5d9ff539860ef70abd1fc4e96e9ac1bdf6405f3daee51228dc7b41cb07e25d","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-26T08:36:39Z","title_canon_sha256":"586e5487b3a6927543259c2dd5ac950e1af3533a7a75737a7e71053026bd0e8e"},"schema_version":"1.0","source":{"id":"2310.17238","kind":"arxiv","version":1}},"canonical_sha256":"32df59b6028a212235224f17678c246023b5854338ac154effbb60131af467d8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"32df59b6028a212235224f17678c246023b5854338ac154effbb60131af467d8","first_computed_at":"2026-07-05T07:05:21.926910Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:05:21.926910Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ttJBgMvvpQxzfy1XLHDlAdj84gibiIA9TJeFETnFg91n/DQ3zWtP+sBW+JiqP4ooh+8jdodGs7npEUhUEm+6Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:05:21.927779Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.17238","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a56976356eb5f2b507271f4d1088269e5959ed6d7839df674621893dedd0e623","sha256:ed046ec15d1377e91539e77742db436258f8277a70fd590d26139ef38618c9f9"],"state_sha256":"20322859012cab1059bb28db37105293664cd9f3334c5def79518b9b8cdef6aa"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Slh/buJ6Z/jD5gk2IQ88Qtq50f45pDjVFMXPi6SWsPcbGxhh76CNWzW7xuo9g146s9roAHJl7mluvXkZ6RRZBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-10T00:55:34.720679Z","bundle_sha256":"e7bf9a322c655c14d2798c5976b2920463d0ec6b0ec4f95d685da7034d2aa983"}}