{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:L4K7GLVPYL7AU2XGMWLZ5BW5QW","short_pith_number":"pith:L4K7GLVP","canonical_record":{"source":{"id":"1811.05475","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-11-13T17:31:49Z","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"title_canon_sha256":"1e759abc6ff6d25bb3041a494266e071c331218a220e6f61fa12e7cd954fb4e5","abstract_canon_sha256":"a46fce00ee6288697c64302a90bfa9c2204d366fb8071a41f35aeeecc6db7430"},"schema_version":"1.0"},"canonical_sha256":"5f15f32eafc2fe0a6ae665979e86dd858be41dc478f96d16f0494e90b13a6a76","source":{"kind":"arxiv","id":"1811.05475","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.05475","created_at":"2026-05-17T23:41:53Z"},{"alias_kind":"arxiv_version","alias_value":"1811.05475v2","created_at":"2026-05-17T23:41:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.05475","created_at":"2026-05-17T23:41:53Z"},{"alias_kind":"pith_short_12","alias_value":"L4K7GLVPYL7A","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"L4K7GLVPYL7AU2XG","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"L4K7GLVP","created_at":"2026-05-18T12:32:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:L4K7GLVPYL7AU2XGMWLZ5BW5QW","target":"record","payload":{"canonical_record":{"source":{"id":"1811.05475","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-11-13T17:31:49Z","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"title_canon_sha256":"1e759abc6ff6d25bb3041a494266e071c331218a220e6f61fa12e7cd954fb4e5","abstract_canon_sha256":"a46fce00ee6288697c64302a90bfa9c2204d366fb8071a41f35aeeecc6db7430"},"schema_version":"1.0"},"canonical_sha256":"5f15f32eafc2fe0a6ae665979e86dd858be41dc478f96d16f0494e90b13a6a76","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:53.715778Z","signature_b64":"d32tV/wj9vX0SIww4ROI5X+10soautmp9KAorOcdU1InoSHT5lSPIs2z7l0Kg26VOUwjTu+Bbw2dFJ/kqfrVBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5f15f32eafc2fe0a6ae665979e86dd858be41dc478f96d16f0494e90b13a6a76","last_reissued_at":"2026-05-17T23:41:53.715114Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:53.715114Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.05475","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":"2026-05-17T23:41:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"du3KWjtUCoz5Q8X/bdg+Tsp13jmE7rBY7q9cO3sh0rBhhzQsJ0tK665sEeNAqL8VANLnfd7WM6U+VvdKaJScBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T11:21:07.768789Z"},"content_sha256":"a0d85a0f7cd066d5906d81c1795459fc526298f8386b65c3baa1f5564453229d","schema_version":"1.0","event_id":"sha256:a0d85a0f7cd066d5906d81c1795459fc526298f8386b65c3baa1f5564453229d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:L4K7GLVPYL7AU2XGMWLZ5BW5QW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ML-Net: multi-label classification of biomedical texts with deep neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG","stat.ML"],"primary_cat":"cs.IR","authors_text":"Cui Tao, Jingcheng Du, Qingyu Chen, Yang Xiang, Yifan Peng, Zhiyong Lu","submitted_at":"2018-11-13T17:31:49Z","abstract_excerpt":"In multi-label text classification, each textual document can be assigned with one or more labels. Due to this nature, the multi-label text classification task is often considered to be more challenging compared to the binary or multi-class text classification problems. As an important task with broad applications in biomedicine such as assigning diagnosis codes, a number of different computational methods (e.g. training and combining binary classifiers for each label) have been proposed in recent years. However, many suffered from modest accuracy and efficiency, with only limited success in p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.05475","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":"2026-05-17T23:41:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"81pkrBK/BBnl26fyqX6+S3ktG+lNRybNRqhgX/0M1exuSVHqs8xi1VIFWCPVuGh8M6oOYSOpceoqs9jTuNaDDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T11:21:07.769363Z"},"content_sha256":"68df1a94bc35a32a8fb2a2eddd4427820996b6bbaf9cabeef588bb0ebff15b4a","schema_version":"1.0","event_id":"sha256:68df1a94bc35a32a8fb2a2eddd4427820996b6bbaf9cabeef588bb0ebff15b4a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L4K7GLVPYL7AU2XGMWLZ5BW5QW/bundle.json","state_url":"https://pith.science/pith/L4K7GLVPYL7AU2XGMWLZ5BW5QW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L4K7GLVPYL7AU2XGMWLZ5BW5QW/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":"2026-05-27T11:21:07Z","links":{"resolver":"https://pith.science/pith/L4K7GLVPYL7AU2XGMWLZ5BW5QW","bundle":"https://pith.science/pith/L4K7GLVPYL7AU2XGMWLZ5BW5QW/bundle.json","state":"https://pith.science/pith/L4K7GLVPYL7AU2XGMWLZ5BW5QW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L4K7GLVPYL7AU2XGMWLZ5BW5QW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:L4K7GLVPYL7AU2XGMWLZ5BW5QW","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":"a46fce00ee6288697c64302a90bfa9c2204d366fb8071a41f35aeeecc6db7430","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-11-13T17:31:49Z","title_canon_sha256":"1e759abc6ff6d25bb3041a494266e071c331218a220e6f61fa12e7cd954fb4e5"},"schema_version":"1.0","source":{"id":"1811.05475","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.05475","created_at":"2026-05-17T23:41:53Z"},{"alias_kind":"arxiv_version","alias_value":"1811.05475v2","created_at":"2026-05-17T23:41:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.05475","created_at":"2026-05-17T23:41:53Z"},{"alias_kind":"pith_short_12","alias_value":"L4K7GLVPYL7A","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"L4K7GLVPYL7AU2XG","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"L4K7GLVP","created_at":"2026-05-18T12:32:33Z"}],"graph_snapshots":[{"event_id":"sha256:68df1a94bc35a32a8fb2a2eddd4427820996b6bbaf9cabeef588bb0ebff15b4a","target":"graph","created_at":"2026-05-17T23:41:53Z","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":"In multi-label text classification, each textual document can be assigned with one or more labels. Due to this nature, the multi-label text classification task is often considered to be more challenging compared to the binary or multi-class text classification problems. As an important task with broad applications in biomedicine such as assigning diagnosis codes, a number of different computational methods (e.g. training and combining binary classifiers for each label) have been proposed in recent years. However, many suffered from modest accuracy and efficiency, with only limited success in p","authors_text":"Cui Tao, Jingcheng Du, Qingyu Chen, Yang Xiang, Yifan Peng, Zhiyong Lu","cross_cats":["cs.CL","cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-11-13T17:31:49Z","title":"ML-Net: multi-label classification of biomedical texts with deep neural networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.05475","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:a0d85a0f7cd066d5906d81c1795459fc526298f8386b65c3baa1f5564453229d","target":"record","created_at":"2026-05-17T23:41:53Z","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":"a46fce00ee6288697c64302a90bfa9c2204d366fb8071a41f35aeeecc6db7430","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-11-13T17:31:49Z","title_canon_sha256":"1e759abc6ff6d25bb3041a494266e071c331218a220e6f61fa12e7cd954fb4e5"},"schema_version":"1.0","source":{"id":"1811.05475","kind":"arxiv","version":2}},"canonical_sha256":"5f15f32eafc2fe0a6ae665979e86dd858be41dc478f96d16f0494e90b13a6a76","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5f15f32eafc2fe0a6ae665979e86dd858be41dc478f96d16f0494e90b13a6a76","first_computed_at":"2026-05-17T23:41:53.715114Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:41:53.715114Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"d32tV/wj9vX0SIww4ROI5X+10soautmp9KAorOcdU1InoSHT5lSPIs2z7l0Kg26VOUwjTu+Bbw2dFJ/kqfrVBQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:41:53.715778Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.05475","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a0d85a0f7cd066d5906d81c1795459fc526298f8386b65c3baa1f5564453229d","sha256:68df1a94bc35a32a8fb2a2eddd4427820996b6bbaf9cabeef588bb0ebff15b4a"],"state_sha256":"67576c6662d77ff1b8783f41927a53fb38111070638282be4dc0e5b3f6af03f0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fNAWjXUlKCJBI47I3defbG3RY+3VyzaE7NkmT7LxhI6G4+GP10eajKC5EHCqHCj+apCgViPeTgfdPOv2CUHXAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T11:21:07.773904Z","bundle_sha256":"dcfaa35b5a50740bc4725719301dac3b954ba63c63078659df8dbc8cebc89938"}}