{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4ZSITON7FEEKHR4IQRWBIPBFZ2","short_pith_number":"pith:4ZSITON7","schema_version":"1.0","canonical_sha256":"e66489b9bf2908a3c788846c143c25ce90c1b64525fe01381289fff0d31e4a1e","source":{"kind":"arxiv","id":"2506.01488","version":1},"attestation_state":"computed","paper":{"title":"Argument-Centric Causal Intervention Method for Mitigating Bias in Cross-Document Event Coreference Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Fuyuan Wei, Hongzhen Lv, Jiaren Peng, Liejun Wang, Long Yao, Wenzhong Yang, Xiaoming Tao, Yabo Yin","submitted_at":"2025-06-02T09:46:59Z","abstract_excerpt":"Cross-document Event Coreference Resolution (CD-ECR) is a fundamental task in natural language processing (NLP) that seeks to determine whether event mentions across multiple documents refer to the same real-world occurrence. However, current CD-ECR approaches predominantly rely on trigger features within input mention pairs, which induce spurious correlations between surface-level lexical features and coreference relationships, impairing the overall performance of the models. To address this issue, we propose a novel cross-document event coreference resolution method based on Argument-Centric"},"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":"2506.01488","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-02T09:46:59Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"73ddcb763d1338a5883e1a314ce2705868852506af69f55c4543bb82e4a6ca82","abstract_canon_sha256":"8c8eff448a0494a783f4daa50c1423eba0f01469dd84a4f75e85c5d296fa8cd1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:14:14.515100Z","signature_b64":"usIylRlTsA+pb62/y9eQ1Geth2BxVNlSKOmohTI0LX2jCQ/wsueQ961afCzn7OQu0bPf88UpxSN2stl2I3nqBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e66489b9bf2908a3c788846c143c25ce90c1b64525fe01381289fff0d31e4a1e","last_reissued_at":"2026-07-05T11:14:14.514639Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:14:14.514639Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Argument-Centric Causal Intervention Method for Mitigating Bias in Cross-Document Event Coreference Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Fuyuan Wei, Hongzhen Lv, Jiaren Peng, Liejun Wang, Long Yao, Wenzhong Yang, Xiaoming Tao, Yabo Yin","submitted_at":"2025-06-02T09:46:59Z","abstract_excerpt":"Cross-document Event Coreference Resolution (CD-ECR) is a fundamental task in natural language processing (NLP) that seeks to determine whether event mentions across multiple documents refer to the same real-world occurrence. However, current CD-ECR approaches predominantly rely on trigger features within input mention pairs, which induce spurious correlations between surface-level lexical features and coreference relationships, impairing the overall performance of the models. To address this issue, we propose a novel cross-document event coreference resolution method based on Argument-Centric"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.01488","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/2506.01488/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":"2506.01488","created_at":"2026-07-05T11:14:14.514696+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.01488v1","created_at":"2026-07-05T11:14:14.514696+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.01488","created_at":"2026-07-05T11:14:14.514696+00:00"},{"alias_kind":"pith_short_12","alias_value":"4ZSITON7FEEK","created_at":"2026-07-05T11:14:14.514696+00:00"},{"alias_kind":"pith_short_16","alias_value":"4ZSITON7FEEKHR4I","created_at":"2026-07-05T11:14:14.514696+00:00"},{"alias_kind":"pith_short_8","alias_value":"4ZSITON7","created_at":"2026-07-05T11:14:14.514696+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/4ZSITON7FEEKHR4IQRWBIPBFZ2","json":"https://pith.science/pith/4ZSITON7FEEKHR4IQRWBIPBFZ2.json","graph_json":"https://pith.science/api/pith-number/4ZSITON7FEEKHR4IQRWBIPBFZ2/graph.json","events_json":"https://pith.science/api/pith-number/4ZSITON7FEEKHR4IQRWBIPBFZ2/events.json","paper":"https://pith.science/paper/4ZSITON7"},"agent_actions":{"view_html":"https://pith.science/pith/4ZSITON7FEEKHR4IQRWBIPBFZ2","download_json":"https://pith.science/pith/4ZSITON7FEEKHR4IQRWBIPBFZ2.json","view_paper":"https://pith.science/paper/4ZSITON7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.01488&json=true","fetch_graph":"https://pith.science/api/pith-number/4ZSITON7FEEKHR4IQRWBIPBFZ2/graph.json","fetch_events":"https://pith.science/api/pith-number/4ZSITON7FEEKHR4IQRWBIPBFZ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4ZSITON7FEEKHR4IQRWBIPBFZ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4ZSITON7FEEKHR4IQRWBIPBFZ2/action/storage_attestation","attest_author":"https://pith.science/pith/4ZSITON7FEEKHR4IQRWBIPBFZ2/action/author_attestation","sign_citation":"https://pith.science/pith/4ZSITON7FEEKHR4IQRWBIPBFZ2/action/citation_signature","submit_replication":"https://pith.science/pith/4ZSITON7FEEKHR4IQRWBIPBFZ2/action/replication_record"}},"created_at":"2026-07-05T11:14:14.514696+00:00","updated_at":"2026-07-05T11:14:14.514696+00:00"}