{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:ILZ6W26KLFKE2J3HVB2P4WWIPB","short_pith_number":"pith:ILZ6W26K","canonical_record":{"source":{"id":"1609.00718","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-08-31T15:43:27Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"4e0058ef57c9223c36b32db1afe1f067cc37886f9309d4fbc83c8794d26d329c","abstract_canon_sha256":"9fdedf3eb04e691c42194ea81504c4d4ca9762cef197122e1ec66d10fcab1e8a"},"schema_version":"1.0"},"canonical_sha256":"42f3eb6bca59544d2767a874fe5ac8787fcd92e18142a540d340f1f04ae85d8a","source":{"kind":"arxiv","id":"1609.00718","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1609.00718","created_at":"2026-05-18T01:06:23Z"},{"alias_kind":"arxiv_version","alias_value":"1609.00718v1","created_at":"2026-05-18T01:06:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.00718","created_at":"2026-05-18T01:06:23Z"},{"alias_kind":"pith_short_12","alias_value":"ILZ6W26KLFKE","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_16","alias_value":"ILZ6W26KLFKE2J3H","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_8","alias_value":"ILZ6W26K","created_at":"2026-05-18T12:30:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:ILZ6W26KLFKE2J3HVB2P4WWIPB","target":"record","payload":{"canonical_record":{"source":{"id":"1609.00718","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-08-31T15:43:27Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"4e0058ef57c9223c36b32db1afe1f067cc37886f9309d4fbc83c8794d26d329c","abstract_canon_sha256":"9fdedf3eb04e691c42194ea81504c4d4ca9762cef197122e1ec66d10fcab1e8a"},"schema_version":"1.0"},"canonical_sha256":"42f3eb6bca59544d2767a874fe5ac8787fcd92e18142a540d340f1f04ae85d8a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:06:23.382329Z","signature_b64":"9wys3Ve40v7J2pZXYHy3o6XpqiLLue4UaQuQ5ZK8bvfq9+XOS5W0Ngp3NtG9JTJxokvGPoiCPsu/OD3trNAoCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"42f3eb6bca59544d2767a874fe5ac8787fcd92e18142a540d340f1f04ae85d8a","last_reissued_at":"2026-05-18T01:06:23.381855Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:06:23.381855Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1609.00718","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":"2026-05-18T01:06:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"f1DBSGS2UOwqb8+GB3WPHK8H4kN11Emm1Of71cGmLqRrv/IXFZQEAAyvvoArqM8biFJXi6isIcEwDQPGtwhRCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T00:57:05.466113Z"},"content_sha256":"ca645499c80cf96514bf6ad2853b29743505cc01908c346dbf874c13d3966394","schema_version":"1.0","event_id":"sha256:ca645499c80cf96514bf6ad2853b29743505cc01908c346dbf874c13d3966394"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:ILZ6W26KLFKE2J3HVB2P4WWIPB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Convolutional Neural Networks for Text Categorization: Shallow Word-level vs. Deep Character-level","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CL","authors_text":"Rie Johnson, Tong Zhang","submitted_at":"2016-08-31T15:43:27Z","abstract_excerpt":"This paper reports the performances of shallow word-level convolutional neural networks (CNN), our earlier work (2015), on the eight datasets with relatively large training data that were used for testing the very deep character-level CNN in Conneau et al. (2016). Our findings are as follows. The shallow word-level CNNs achieve better error rates than the error rates reported in Conneau et al., though the results should be interpreted with some consideration due to the unique pre-processing of Conneau et al. The shallow word-level CNN uses more parameters and therefore requires more storage th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.00718","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":"2026-05-18T01:06:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eKGnJ+maicYOSKI5j7TeVLN27JXd7xUmIuISTMbL36ydZAFsWZ7VVasfX8OC8KrIS7urSPqdPZgo6StRMJVFAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T00:57:05.466862Z"},"content_sha256":"6116ab63b27648ef6d83cb5f84ae178bb88db874defb62fc5f90b78c14569259","schema_version":"1.0","event_id":"sha256:6116ab63b27648ef6d83cb5f84ae178bb88db874defb62fc5f90b78c14569259"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ILZ6W26KLFKE2J3HVB2P4WWIPB/bundle.json","state_url":"https://pith.science/pith/ILZ6W26KLFKE2J3HVB2P4WWIPB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ILZ6W26KLFKE2J3HVB2P4WWIPB/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":"2026-06-12T00:57:05Z","links":{"resolver":"https://pith.science/pith/ILZ6W26KLFKE2J3HVB2P4WWIPB","bundle":"https://pith.science/pith/ILZ6W26KLFKE2J3HVB2P4WWIPB/bundle.json","state":"https://pith.science/pith/ILZ6W26KLFKE2J3HVB2P4WWIPB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ILZ6W26KLFKE2J3HVB2P4WWIPB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:ILZ6W26KLFKE2J3HVB2P4WWIPB","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":"9fdedf3eb04e691c42194ea81504c4d4ca9762cef197122e1ec66d10fcab1e8a","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-08-31T15:43:27Z","title_canon_sha256":"4e0058ef57c9223c36b32db1afe1f067cc37886f9309d4fbc83c8794d26d329c"},"schema_version":"1.0","source":{"id":"1609.00718","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1609.00718","created_at":"2026-05-18T01:06:23Z"},{"alias_kind":"arxiv_version","alias_value":"1609.00718v1","created_at":"2026-05-18T01:06:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.00718","created_at":"2026-05-18T01:06:23Z"},{"alias_kind":"pith_short_12","alias_value":"ILZ6W26KLFKE","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_16","alias_value":"ILZ6W26KLFKE2J3H","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_8","alias_value":"ILZ6W26K","created_at":"2026-05-18T12:30:22Z"}],"graph_snapshots":[{"event_id":"sha256:6116ab63b27648ef6d83cb5f84ae178bb88db874defb62fc5f90b78c14569259","target":"graph","created_at":"2026-05-18T01:06:23Z","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":"This paper reports the performances of shallow word-level convolutional neural networks (CNN), our earlier work (2015), on the eight datasets with relatively large training data that were used for testing the very deep character-level CNN in Conneau et al. (2016). Our findings are as follows. The shallow word-level CNNs achieve better error rates than the error rates reported in Conneau et al., though the results should be interpreted with some consideration due to the unique pre-processing of Conneau et al. The shallow word-level CNN uses more parameters and therefore requires more storage th","authors_text":"Rie Johnson, Tong Zhang","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-08-31T15:43:27Z","title":"Convolutional Neural Networks for Text Categorization: Shallow Word-level vs. Deep Character-level"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.00718","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:ca645499c80cf96514bf6ad2853b29743505cc01908c346dbf874c13d3966394","target":"record","created_at":"2026-05-18T01:06:23Z","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":"9fdedf3eb04e691c42194ea81504c4d4ca9762cef197122e1ec66d10fcab1e8a","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-08-31T15:43:27Z","title_canon_sha256":"4e0058ef57c9223c36b32db1afe1f067cc37886f9309d4fbc83c8794d26d329c"},"schema_version":"1.0","source":{"id":"1609.00718","kind":"arxiv","version":1}},"canonical_sha256":"42f3eb6bca59544d2767a874fe5ac8787fcd92e18142a540d340f1f04ae85d8a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"42f3eb6bca59544d2767a874fe5ac8787fcd92e18142a540d340f1f04ae85d8a","first_computed_at":"2026-05-18T01:06:23.381855Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:06:23.381855Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9wys3Ve40v7J2pZXYHy3o6XpqiLLue4UaQuQ5ZK8bvfq9+XOS5W0Ngp3NtG9JTJxokvGPoiCPsu/OD3trNAoCg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:06:23.382329Z","signed_message":"canonical_sha256_bytes"},"source_id":"1609.00718","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ca645499c80cf96514bf6ad2853b29743505cc01908c346dbf874c13d3966394","sha256:6116ab63b27648ef6d83cb5f84ae178bb88db874defb62fc5f90b78c14569259"],"state_sha256":"b32a1da91b51ce3eb3a70c620b9fa79d6dc6fbf3337a00b0130334c0ee60c0bd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Sp9r3PHJPF0h8Et3InUwjXTtrX3fPzyI0C5f3k5LOxKH9UH/MlToOlJ+irkEA9Yooday1I3bvAffj1HVeYRcAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-12T00:57:05.472685Z","bundle_sha256":"fdaf0a82d7602529779bc74b9809521e744627a98a6e7e7d2de76a98f561670c"}}