{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:WA54TQEBYMQX3GOTWZJ7ZTATPB","short_pith_number":"pith:WA54TQEB","canonical_record":{"source":{"id":"1906.04898","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-06-09T07:23:45Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"d0ca0df10ecb7b244867cfae9819d469795f50efb24b4e0b1f223d7652831a84","abstract_canon_sha256":"e60d34918d167151f55eb56de47c4b3b796158758c1d2036fd9dbd810a357ac8"},"schema_version":"1.0"},"canonical_sha256":"b03bc9c081c3217d99d3b653fccc137857a41050a3be2fc105430f4a751016b3","source":{"kind":"arxiv","id":"1906.04898","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.04898","created_at":"2026-05-17T23:43:30Z"},{"alias_kind":"arxiv_version","alias_value":"1906.04898v1","created_at":"2026-05-17T23:43:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.04898","created_at":"2026-05-17T23:43:30Z"},{"alias_kind":"pith_short_12","alias_value":"WA54TQEBYMQX","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"WA54TQEBYMQX3GOT","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"WA54TQEB","created_at":"2026-05-18T12:33:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:WA54TQEBYMQX3GOTWZJ7ZTATPB","target":"record","payload":{"canonical_record":{"source":{"id":"1906.04898","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-06-09T07:23:45Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"d0ca0df10ecb7b244867cfae9819d469795f50efb24b4e0b1f223d7652831a84","abstract_canon_sha256":"e60d34918d167151f55eb56de47c4b3b796158758c1d2036fd9dbd810a357ac8"},"schema_version":"1.0"},"canonical_sha256":"b03bc9c081c3217d99d3b653fccc137857a41050a3be2fc105430f4a751016b3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:30.184967Z","signature_b64":"AMtGgEjzKz+7BTPbPv+S/rFAapJDl+8b5puOeyUx4NLwFzDvmsMJBnu8ePHl4WQvMUzwjhukAvHcII4Xu7zyBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b03bc9c081c3217d99d3b653fccc137857a41050a3be2fc105430f4a751016b3","last_reissued_at":"2026-05-17T23:43:30.184268Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:30.184268Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.04898","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:43:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1izF5kaJFgQA8VR2o9V4rElkdCILR4sgszYNb1yBsNss+lyg7Kio5Jhwq6g+FpLVdiRbc4YwwgDE/AIsoi+2Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T22:18:36.851995Z"},"content_sha256":"85ae4af63e4cf67c0d4badd92a8a0174f5519d278251fe1d0fbbe0a65b9ced80","schema_version":"1.0","event_id":"sha256:85ae4af63e4cf67c0d4badd92a8a0174f5519d278251fe1d0fbbe0a65b9ced80"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:WA54TQEBYMQX3GOTWZJ7ZTATPB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.IR","authors_text":"Bo Li, Hao Peng, Jianxin Li, Lifang He, Lihong Wang, Philip S. Yu, Qiran Gong, Senzhang Wang","submitted_at":"2019-06-09T07:23:45Z","abstract_excerpt":"CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation learning and are widely used in various text mining tasks such as large-scale multi-label text classification. However, most existing deep models for multi-label text classification consider either the non-consecutive and long-distance semantics or the sequential semantics, but how to consider them both coherently is less studied. In addition, most existing methods treat output labels as independent methods, but ignore the hierarchical relations among them, leading to useful semantic information loss. In this pape"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.04898","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:43:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4+4oexhD6R7CPdPiKJnfTFXRn3uub1HzyXWQZfaQC+OCkUtr1tDgKltY7RFjsmBaZJmYZGM7pS5c2yiFsOIQCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T22:18:36.852506Z"},"content_sha256":"485e6b72634ba67a13362292d0743d1bd2eda97603fbc0ba4ebf554ee4ee683f","schema_version":"1.0","event_id":"sha256:485e6b72634ba67a13362292d0743d1bd2eda97603fbc0ba4ebf554ee4ee683f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WA54TQEBYMQX3GOTWZJ7ZTATPB/bundle.json","state_url":"https://pith.science/pith/WA54TQEBYMQX3GOTWZJ7ZTATPB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WA54TQEBYMQX3GOTWZJ7ZTATPB/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":"2026-05-27T22:18:36Z","links":{"resolver":"https://pith.science/pith/WA54TQEBYMQX3GOTWZJ7ZTATPB","bundle":"https://pith.science/pith/WA54TQEBYMQX3GOTWZJ7ZTATPB/bundle.json","state":"https://pith.science/pith/WA54TQEBYMQX3GOTWZJ7ZTATPB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WA54TQEBYMQX3GOTWZJ7ZTATPB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:WA54TQEBYMQX3GOTWZJ7ZTATPB","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":"e60d34918d167151f55eb56de47c4b3b796158758c1d2036fd9dbd810a357ac8","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-06-09T07:23:45Z","title_canon_sha256":"d0ca0df10ecb7b244867cfae9819d469795f50efb24b4e0b1f223d7652831a84"},"schema_version":"1.0","source":{"id":"1906.04898","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.04898","created_at":"2026-05-17T23:43:30Z"},{"alias_kind":"arxiv_version","alias_value":"1906.04898v1","created_at":"2026-05-17T23:43:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.04898","created_at":"2026-05-17T23:43:30Z"},{"alias_kind":"pith_short_12","alias_value":"WA54TQEBYMQX","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"WA54TQEBYMQX3GOT","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"WA54TQEB","created_at":"2026-05-18T12:33:30Z"}],"graph_snapshots":[{"event_id":"sha256:485e6b72634ba67a13362292d0743d1bd2eda97603fbc0ba4ebf554ee4ee683f","target":"graph","created_at":"2026-05-17T23:43:30Z","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":"CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation learning and are widely used in various text mining tasks such as large-scale multi-label text classification. However, most existing deep models for multi-label text classification consider either the non-consecutive and long-distance semantics or the sequential semantics, but how to consider them both coherently is less studied. In addition, most existing methods treat output labels as independent methods, but ignore the hierarchical relations among them, leading to useful semantic information loss. In this pape","authors_text":"Bo Li, Hao Peng, Jianxin Li, Lifang He, Lihong Wang, Philip S. Yu, Qiran Gong, Senzhang Wang","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-06-09T07:23:45Z","title":"Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.04898","kind":"arxiv","version":1},"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":"sha256:85ae4af63e4cf67c0d4badd92a8a0174f5519d278251fe1d0fbbe0a65b9ced80","target":"record","created_at":"2026-05-17T23:43:30Z","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":"e60d34918d167151f55eb56de47c4b3b796158758c1d2036fd9dbd810a357ac8","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-06-09T07:23:45Z","title_canon_sha256":"d0ca0df10ecb7b244867cfae9819d469795f50efb24b4e0b1f223d7652831a84"},"schema_version":"1.0","source":{"id":"1906.04898","kind":"arxiv","version":1}},"canonical_sha256":"b03bc9c081c3217d99d3b653fccc137857a41050a3be2fc105430f4a751016b3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b03bc9c081c3217d99d3b653fccc137857a41050a3be2fc105430f4a751016b3","first_computed_at":"2026-05-17T23:43:30.184268Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:43:30.184268Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AMtGgEjzKz+7BTPbPv+S/rFAapJDl+8b5puOeyUx4NLwFzDvmsMJBnu8ePHl4WQvMUzwjhukAvHcII4Xu7zyBg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:43:30.184967Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.04898","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:85ae4af63e4cf67c0d4badd92a8a0174f5519d278251fe1d0fbbe0a65b9ced80","sha256:485e6b72634ba67a13362292d0743d1bd2eda97603fbc0ba4ebf554ee4ee683f"],"state_sha256":"772a54f2bb2f4879dc0c9231464b5f5b3e04780f7d61fe7fe548fcc0c529ac4a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"h2Qf9gP85kELZu1B6MR//CtOOD6tkAQp/4Y66n9sSIQ+a5G56z92iOOYJ5RGB2HgYYZyglSYU36efTkoWvauBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T22:18:36.855727Z","bundle_sha256":"9c5d2e34b22c06b6636686afc2925612426c64aae6823d87ed70449a299d61a3"}}