{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:QW3SNYCZVGHIDCYDCSS4E3QWMZ","short_pith_number":"pith:QW3SNYCZ","schema_version":"1.0","canonical_sha256":"85b726e059a98e818b0314a5c26e16667b85e29073b61f8d2db8ff80c9890a3b","source":{"kind":"arxiv","id":"1908.05908","version":2},"attestation_state":"computed","paper":{"title":"BERT-Based Multi-Head Selection for Joint Entity-Relation Extraction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Taifeng Wang, Wei Chu, Weipeng Huang, Xingyi Cheng","submitted_at":"2019-08-16T09:29:43Z","abstract_excerpt":"In this paper, we report our method for the Information Extraction task in 2019 Language and Intelligence Challenge. We incorporate BERT into the multi-head selection framework for joint entity-relation extraction. This model extends existing approaches from three perspectives. First, BERT is adopted as a feature extraction layer at the bottom of the multi-head selection framework. We further optimize BERT by introducing a semantic-enhanced task during BERT pre-training. Second, we introduce a large-scale Baidu Baike corpus for entity recognition pre-training, which is of weekly supervised lea"},"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":"1908.05908","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-16T09:29:43Z","cross_cats_sorted":[],"title_canon_sha256":"eecf79f52425dcbeb1fb2cbd4004a017ecee45d34d90b5e79cf564c727647f89","abstract_canon_sha256":"b1b9224ee29b834dd7c464aae81c13cfce6f9bb66363a799e93a240503f1da15"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:07:24.437271Z","signature_b64":"hdMrbCqO1xNzLtibrXHV1V5Mm7wecEYqeQ9y79aWJqwb0yDg2zX9gBj/dSG2M4NVVCM35D66c+v1JoBIyZRDDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"85b726e059a98e818b0314a5c26e16667b85e29073b61f8d2db8ff80c9890a3b","last_reissued_at":"2026-07-05T00:07:24.436858Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:07:24.436858Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BERT-Based Multi-Head Selection for Joint Entity-Relation Extraction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Taifeng Wang, Wei Chu, Weipeng Huang, Xingyi Cheng","submitted_at":"2019-08-16T09:29:43Z","abstract_excerpt":"In this paper, we report our method for the Information Extraction task in 2019 Language and Intelligence Challenge. We incorporate BERT into the multi-head selection framework for joint entity-relation extraction. This model extends existing approaches from three perspectives. First, BERT is adopted as a feature extraction layer at the bottom of the multi-head selection framework. We further optimize BERT by introducing a semantic-enhanced task during BERT pre-training. Second, we introduce a large-scale Baidu Baike corpus for entity recognition pre-training, which is of weekly supervised lea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.05908","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/1908.05908/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":"1908.05908","created_at":"2026-07-05T00:07:24.436913+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.05908v2","created_at":"2026-07-05T00:07:24.436913+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.05908","created_at":"2026-07-05T00:07:24.436913+00:00"},{"alias_kind":"pith_short_12","alias_value":"QW3SNYCZVGHI","created_at":"2026-07-05T00:07:24.436913+00:00"},{"alias_kind":"pith_short_16","alias_value":"QW3SNYCZVGHIDCYD","created_at":"2026-07-05T00:07:24.436913+00:00"},{"alias_kind":"pith_short_8","alias_value":"QW3SNYCZ","created_at":"2026-07-05T00:07:24.436913+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/QW3SNYCZVGHIDCYDCSS4E3QWMZ","json":"https://pith.science/pith/QW3SNYCZVGHIDCYDCSS4E3QWMZ.json","graph_json":"https://pith.science/api/pith-number/QW3SNYCZVGHIDCYDCSS4E3QWMZ/graph.json","events_json":"https://pith.science/api/pith-number/QW3SNYCZVGHIDCYDCSS4E3QWMZ/events.json","paper":"https://pith.science/paper/QW3SNYCZ"},"agent_actions":{"view_html":"https://pith.science/pith/QW3SNYCZVGHIDCYDCSS4E3QWMZ","download_json":"https://pith.science/pith/QW3SNYCZVGHIDCYDCSS4E3QWMZ.json","view_paper":"https://pith.science/paper/QW3SNYCZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.05908&json=true","fetch_graph":"https://pith.science/api/pith-number/QW3SNYCZVGHIDCYDCSS4E3QWMZ/graph.json","fetch_events":"https://pith.science/api/pith-number/QW3SNYCZVGHIDCYDCSS4E3QWMZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QW3SNYCZVGHIDCYDCSS4E3QWMZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QW3SNYCZVGHIDCYDCSS4E3QWMZ/action/storage_attestation","attest_author":"https://pith.science/pith/QW3SNYCZVGHIDCYDCSS4E3QWMZ/action/author_attestation","sign_citation":"https://pith.science/pith/QW3SNYCZVGHIDCYDCSS4E3QWMZ/action/citation_signature","submit_replication":"https://pith.science/pith/QW3SNYCZVGHIDCYDCSS4E3QWMZ/action/replication_record"}},"created_at":"2026-07-05T00:07:24.436913+00:00","updated_at":"2026-07-05T00:07:24.436913+00:00"}