{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:HOFMTPWYK5BZXHRLXMETIP2GLT","short_pith_number":"pith:HOFMTPWY","schema_version":"1.0","canonical_sha256":"3b8ac9bed857439b9e2bbb09343f465ccf5b91807a767479da5f887de1d25727","source":{"kind":"arxiv","id":"1803.05662","version":1},"attestation_state":"computed","paper":{"title":"Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ji Wen, Qi Su, Xuancheng Ren, Xu Sun","submitted_at":"2018-03-15T09:45:58Z","abstract_excerpt":"Relation classification is an important semantic processing task in the field of natural language processing. In this paper, we propose the task of relation classification for Chinese literature text. A new dataset of Chinese literature text is constructed to facilitate the study in this task. We present a novel model, named Structure Regularized Bidirectional Recurrent Convolutional Neural Network (SR-BRCNN), to identify the relation between entities. The proposed model learns relation representations along the shortest dependency path (SDP) extracted from the structure regularized dependency"},"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":"1803.05662","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-03-15T09:45:58Z","cross_cats_sorted":[],"title_canon_sha256":"393dab9cbc8aea3f769eb0d8a1107ddd945b1c6da8113b6b7e76cdca1e4d9b37","abstract_canon_sha256":"16424c0b133ed38018b3bb3aa370b18d6edc97d68c84bbea082d713725ca6a75"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:20:54.382752Z","signature_b64":"S3KgcxQMEEfotomMkDxl8lUzjt6FXPZ7nFoAa4ctPu+LM/wxQ30CJQ6WggwVxN1H7l/WdevCVMBshNYw4hlRCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3b8ac9bed857439b9e2bbb09343f465ccf5b91807a767479da5f887de1d25727","last_reissued_at":"2026-05-18T00:20:54.382227Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:20:54.382227Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ji Wen, Qi Su, Xuancheng Ren, Xu Sun","submitted_at":"2018-03-15T09:45:58Z","abstract_excerpt":"Relation classification is an important semantic processing task in the field of natural language processing. In this paper, we propose the task of relation classification for Chinese literature text. A new dataset of Chinese literature text is constructed to facilitate the study in this task. We present a novel model, named Structure Regularized Bidirectional Recurrent Convolutional Neural Network (SR-BRCNN), to identify the relation between entities. The proposed model learns relation representations along the shortest dependency path (SDP) extracted from the structure regularized dependency"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.05662","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":""},"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":"1803.05662","created_at":"2026-05-18T00:20:54.382301+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.05662v1","created_at":"2026-05-18T00:20:54.382301+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.05662","created_at":"2026-05-18T00:20:54.382301+00:00"},{"alias_kind":"pith_short_12","alias_value":"HOFMTPWYK5BZ","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_16","alias_value":"HOFMTPWYK5BZXHRL","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_8","alias_value":"HOFMTPWY","created_at":"2026-05-18T12:32:28.185984+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/HOFMTPWYK5BZXHRLXMETIP2GLT","json":"https://pith.science/pith/HOFMTPWYK5BZXHRLXMETIP2GLT.json","graph_json":"https://pith.science/api/pith-number/HOFMTPWYK5BZXHRLXMETIP2GLT/graph.json","events_json":"https://pith.science/api/pith-number/HOFMTPWYK5BZXHRLXMETIP2GLT/events.json","paper":"https://pith.science/paper/HOFMTPWY"},"agent_actions":{"view_html":"https://pith.science/pith/HOFMTPWYK5BZXHRLXMETIP2GLT","download_json":"https://pith.science/pith/HOFMTPWYK5BZXHRLXMETIP2GLT.json","view_paper":"https://pith.science/paper/HOFMTPWY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.05662&json=true","fetch_graph":"https://pith.science/api/pith-number/HOFMTPWYK5BZXHRLXMETIP2GLT/graph.json","fetch_events":"https://pith.science/api/pith-number/HOFMTPWYK5BZXHRLXMETIP2GLT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HOFMTPWYK5BZXHRLXMETIP2GLT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HOFMTPWYK5BZXHRLXMETIP2GLT/action/storage_attestation","attest_author":"https://pith.science/pith/HOFMTPWYK5BZXHRLXMETIP2GLT/action/author_attestation","sign_citation":"https://pith.science/pith/HOFMTPWYK5BZXHRLXMETIP2GLT/action/citation_signature","submit_replication":"https://pith.science/pith/HOFMTPWYK5BZXHRLXMETIP2GLT/action/replication_record"}},"created_at":"2026-05-18T00:20:54.382301+00:00","updated_at":"2026-05-18T00:20:54.382301+00:00"}