{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:BALRWSUTAZJ7ZKZKDOS3TC2JB5","short_pith_number":"pith:BALRWSUT","schema_version":"1.0","canonical_sha256":"08171b4a930653fcab2a1ba5b98b490f5e75378ed12eb9adb9ef483029799b95","source":{"kind":"arxiv","id":"2407.01424","version":1},"attestation_state":"computed","paper":{"title":"A Global-Local Attention Mechanism for Relation Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Yiping Sun","submitted_at":"2024-07-01T16:14:25Z","abstract_excerpt":"Relation classification, a crucial component of relation extraction, involves identifying connections between two entities. Previous studies have predominantly focused on integrating the attention mechanism into relation classification at a global scale, overlooking the importance of the local context. To address this gap, this paper introduces a novel global-local attention mechanism for relation classification, which enhances global attention with a localized focus. Additionally, we propose innovative hard and soft localization mechanisms to identify potential keywords for local attention. B"},"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":"2407.01424","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-07-01T16:14:25Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"14f0532ee8f12fec184fa19b1488353eb798a0ab5889d1d6ba7cce0067b44294","abstract_canon_sha256":"e6f78faffcf8fd9d6ba1790212fd36b566e20662b62fd744294495553e5ec143"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:38:47.758263Z","signature_b64":"gijRZ1gB3rBwBB3WM6GRokk+ayOoEi71aaT2z1xCTaFlDVYLVy/KDG9lOn/avPlqw5tTp2uEyfdU4JZqijA9AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"08171b4a930653fcab2a1ba5b98b490f5e75378ed12eb9adb9ef483029799b95","last_reissued_at":"2026-07-05T08:38:47.757787Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:38:47.757787Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Global-Local Attention Mechanism for Relation Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Yiping Sun","submitted_at":"2024-07-01T16:14:25Z","abstract_excerpt":"Relation classification, a crucial component of relation extraction, involves identifying connections between two entities. Previous studies have predominantly focused on integrating the attention mechanism into relation classification at a global scale, overlooking the importance of the local context. To address this gap, this paper introduces a novel global-local attention mechanism for relation classification, which enhances global attention with a localized focus. Additionally, we propose innovative hard and soft localization mechanisms to identify potential keywords for local attention. B"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.01424","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/2407.01424/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":"2407.01424","created_at":"2026-07-05T08:38:47.757846+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.01424v1","created_at":"2026-07-05T08:38:47.757846+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.01424","created_at":"2026-07-05T08:38:47.757846+00:00"},{"alias_kind":"pith_short_12","alias_value":"BALRWSUTAZJ7","created_at":"2026-07-05T08:38:47.757846+00:00"},{"alias_kind":"pith_short_16","alias_value":"BALRWSUTAZJ7ZKZK","created_at":"2026-07-05T08:38:47.757846+00:00"},{"alias_kind":"pith_short_8","alias_value":"BALRWSUT","created_at":"2026-07-05T08:38:47.757846+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.20679","citing_title":"SELF-PERCEPT: Introspection Improves Large Language Models' Detection of Multi-Person Mental Manipulation in Conversations","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BALRWSUTAZJ7ZKZKDOS3TC2JB5","json":"https://pith.science/pith/BALRWSUTAZJ7ZKZKDOS3TC2JB5.json","graph_json":"https://pith.science/api/pith-number/BALRWSUTAZJ7ZKZKDOS3TC2JB5/graph.json","events_json":"https://pith.science/api/pith-number/BALRWSUTAZJ7ZKZKDOS3TC2JB5/events.json","paper":"https://pith.science/paper/BALRWSUT"},"agent_actions":{"view_html":"https://pith.science/pith/BALRWSUTAZJ7ZKZKDOS3TC2JB5","download_json":"https://pith.science/pith/BALRWSUTAZJ7ZKZKDOS3TC2JB5.json","view_paper":"https://pith.science/paper/BALRWSUT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.01424&json=true","fetch_graph":"https://pith.science/api/pith-number/BALRWSUTAZJ7ZKZKDOS3TC2JB5/graph.json","fetch_events":"https://pith.science/api/pith-number/BALRWSUTAZJ7ZKZKDOS3TC2JB5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BALRWSUTAZJ7ZKZKDOS3TC2JB5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BALRWSUTAZJ7ZKZKDOS3TC2JB5/action/storage_attestation","attest_author":"https://pith.science/pith/BALRWSUTAZJ7ZKZKDOS3TC2JB5/action/author_attestation","sign_citation":"https://pith.science/pith/BALRWSUTAZJ7ZKZKDOS3TC2JB5/action/citation_signature","submit_replication":"https://pith.science/pith/BALRWSUTAZJ7ZKZKDOS3TC2JB5/action/replication_record"}},"created_at":"2026-07-05T08:38:47.757846+00:00","updated_at":"2026-07-05T08:38:47.757846+00:00"}