{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:3WGHX4VBZJ5L27V32HEPDBSDFW","short_pith_number":"pith:3WGHX4VB","canonical_record":{"source":{"id":"1508.01006","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-08-05T09:03:46Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"fec7b3f10013f0d2b1f52dea391adf0a59bb198bb8d5ef141c89d0b78e59d393","abstract_canon_sha256":"d3e2027d7f74efffb730d6acfcec465f66b7c564cf887843983deba031543032"},"schema_version":"1.0"},"canonical_sha256":"dd8c7bf2a1ca7abd7ebbd1c8f186432d9b68d8db89453c8748fe7551caa82ad5","source":{"kind":"arxiv","id":"1508.01006","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1508.01006","created_at":"2026-05-18T01:23:44Z"},{"alias_kind":"arxiv_version","alias_value":"1508.01006v2","created_at":"2026-05-18T01:23:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1508.01006","created_at":"2026-05-18T01:23:44Z"},{"alias_kind":"pith_short_12","alias_value":"3WGHX4VBZJ5L","created_at":"2026-05-18T12:29:02Z"},{"alias_kind":"pith_short_16","alias_value":"3WGHX4VBZJ5L27V3","created_at":"2026-05-18T12:29:02Z"},{"alias_kind":"pith_short_8","alias_value":"3WGHX4VB","created_at":"2026-05-18T12:29:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:3WGHX4VBZJ5L27V32HEPDBSDFW","target":"record","payload":{"canonical_record":{"source":{"id":"1508.01006","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-08-05T09:03:46Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"fec7b3f10013f0d2b1f52dea391adf0a59bb198bb8d5ef141c89d0b78e59d393","abstract_canon_sha256":"d3e2027d7f74efffb730d6acfcec465f66b7c564cf887843983deba031543032"},"schema_version":"1.0"},"canonical_sha256":"dd8c7bf2a1ca7abd7ebbd1c8f186432d9b68d8db89453c8748fe7551caa82ad5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:23:44.587095Z","signature_b64":"HR1eBCnhDXmUiIWVGJX/1z8gbfeoV9EyVB0Jij0/pze9L2jO/DQX+c8wITmG4aheSgnwVd/SwBRV1JWDrff1Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dd8c7bf2a1ca7abd7ebbd1c8f186432d9b68d8db89453c8748fe7551caa82ad5","last_reissued_at":"2026-05-18T01:23:44.586520Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:23:44.586520Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1508.01006","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:23:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PSdrVV72klaTFgZQ9/hP0Lo7alnvUWmpe95W+ubcTsFcEIGEduktGUXC0QHrTBc+sUgxfGKPtEvSUCtaWgZRBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T20:57:43.080095Z"},"content_sha256":"916f53bccde4780d89d6fe735cbec840594b1e7108e85f38d1ee98bb4c94d2cb","schema_version":"1.0","event_id":"sha256:916f53bccde4780d89d6fe735cbec840594b1e7108e85f38d1ee98bb4c94d2cb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:3WGHX4VBZJ5L27V32HEPDBSDFW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Relation Classification via Recurrent Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.CL","authors_text":"Dong Wang, Dongxu Zhang","submitted_at":"2015-08-05T09:03:46Z","abstract_excerpt":"Deep learning has gained much success in sentence-level relation classification. For example, convolutional neural networks (CNN) have delivered competitive performance without much effort on feature engineering as the conventional pattern-based methods. Thus a lot of works have been produced based on CNN structures. However, a key issue that has not been well addressed by the CNN-based method is the lack of capability to learn temporal features, especially long-distance dependency between nominal pairs. In this paper, we propose a simple framework based on recurrent neural networks (RNN) and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1508.01006","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":""},"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":"2026-05-18T01:23:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Pw5zfBIixwRkR8c15nMR7rKjEwi3cKskxSQjA4ldWPrivTXjGvfTmhVktUeDS7+Zbus3pLhAv0qRTJPS2ck1AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T20:57:43.080489Z"},"content_sha256":"235a8e5672133734a93e75892daa9aa1a4f250455eb6ee0c46ad005c54b4ac6f","schema_version":"1.0","event_id":"sha256:235a8e5672133734a93e75892daa9aa1a4f250455eb6ee0c46ad005c54b4ac6f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3WGHX4VBZJ5L27V32HEPDBSDFW/bundle.json","state_url":"https://pith.science/pith/3WGHX4VBZJ5L27V32HEPDBSDFW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3WGHX4VBZJ5L27V32HEPDBSDFW/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":"2026-06-08T20:57:43Z","links":{"resolver":"https://pith.science/pith/3WGHX4VBZJ5L27V32HEPDBSDFW","bundle":"https://pith.science/pith/3WGHX4VBZJ5L27V32HEPDBSDFW/bundle.json","state":"https://pith.science/pith/3WGHX4VBZJ5L27V32HEPDBSDFW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3WGHX4VBZJ5L27V32HEPDBSDFW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:3WGHX4VBZJ5L27V32HEPDBSDFW","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":"d3e2027d7f74efffb730d6acfcec465f66b7c564cf887843983deba031543032","cross_cats_sorted":["cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-08-05T09:03:46Z","title_canon_sha256":"fec7b3f10013f0d2b1f52dea391adf0a59bb198bb8d5ef141c89d0b78e59d393"},"schema_version":"1.0","source":{"id":"1508.01006","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1508.01006","created_at":"2026-05-18T01:23:44Z"},{"alias_kind":"arxiv_version","alias_value":"1508.01006v2","created_at":"2026-05-18T01:23:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1508.01006","created_at":"2026-05-18T01:23:44Z"},{"alias_kind":"pith_short_12","alias_value":"3WGHX4VBZJ5L","created_at":"2026-05-18T12:29:02Z"},{"alias_kind":"pith_short_16","alias_value":"3WGHX4VBZJ5L27V3","created_at":"2026-05-18T12:29:02Z"},{"alias_kind":"pith_short_8","alias_value":"3WGHX4VB","created_at":"2026-05-18T12:29:02Z"}],"graph_snapshots":[{"event_id":"sha256:235a8e5672133734a93e75892daa9aa1a4f250455eb6ee0c46ad005c54b4ac6f","target":"graph","created_at":"2026-05-18T01:23:44Z","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"},"paper":{"abstract_excerpt":"Deep learning has gained much success in sentence-level relation classification. For example, convolutional neural networks (CNN) have delivered competitive performance without much effort on feature engineering as the conventional pattern-based methods. Thus a lot of works have been produced based on CNN structures. However, a key issue that has not been well addressed by the CNN-based method is the lack of capability to learn temporal features, especially long-distance dependency between nominal pairs. In this paper, we propose a simple framework based on recurrent neural networks (RNN) and ","authors_text":"Dong Wang, Dongxu Zhang","cross_cats":["cs.LG","cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-08-05T09:03:46Z","title":"Relation Classification via Recurrent Neural Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1508.01006","kind":"arxiv","version":2},"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":"sha256:916f53bccde4780d89d6fe735cbec840594b1e7108e85f38d1ee98bb4c94d2cb","target":"record","created_at":"2026-05-18T01:23:44Z","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":"d3e2027d7f74efffb730d6acfcec465f66b7c564cf887843983deba031543032","cross_cats_sorted":["cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-08-05T09:03:46Z","title_canon_sha256":"fec7b3f10013f0d2b1f52dea391adf0a59bb198bb8d5ef141c89d0b78e59d393"},"schema_version":"1.0","source":{"id":"1508.01006","kind":"arxiv","version":2}},"canonical_sha256":"dd8c7bf2a1ca7abd7ebbd1c8f186432d9b68d8db89453c8748fe7551caa82ad5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dd8c7bf2a1ca7abd7ebbd1c8f186432d9b68d8db89453c8748fe7551caa82ad5","first_computed_at":"2026-05-18T01:23:44.586520Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:23:44.586520Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HR1eBCnhDXmUiIWVGJX/1z8gbfeoV9EyVB0Jij0/pze9L2jO/DQX+c8wITmG4aheSgnwVd/SwBRV1JWDrff1Bw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:23:44.587095Z","signed_message":"canonical_sha256_bytes"},"source_id":"1508.01006","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:916f53bccde4780d89d6fe735cbec840594b1e7108e85f38d1ee98bb4c94d2cb","sha256:235a8e5672133734a93e75892daa9aa1a4f250455eb6ee0c46ad005c54b4ac6f"],"state_sha256":"de30c85a8b063bd1850b6ae5d551f1d172a1af21c701f9ecce647cb210b7e73e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qBn8oyumczZ7XyteRXL8TAbKje6N+OCfSrJYUklvxVIIQ8ewvx5jpR/gJ7P9VoMMMsyyYCkln1xOXu1e+c7VBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T20:57:43.082751Z","bundle_sha256":"180b5c77495e77f0ce7fbf939e17d9cf840bde337e47ca522c7ac8c7fe253fd7"}}