{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:FDW5XTYHQ2M7KYG4BKBRBRYPSG","short_pith_number":"pith:FDW5XTYH","canonical_record":{"source":{"id":"1404.2188","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2014-04-08T15:46:44Z","cross_cats_sorted":[],"title_canon_sha256":"50f96687d7e8da74164c8d608447706d9a55bc8d1fb6c6dc2beaf0d56f9630cb","abstract_canon_sha256":"06e01da8d5dfb1c0a6e276c76d65c847afa26e6fa7b30fa2b723d62dc3b21ec5"},"schema_version":"1.0"},"canonical_sha256":"28eddbcf078699f560dc0a8310c70f91af18fee458e28e4be4ceaf2d9c11674f","source":{"kind":"arxiv","id":"1404.2188","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1404.2188","created_at":"2026-05-18T02:54:38Z"},{"alias_kind":"arxiv_version","alias_value":"1404.2188v1","created_at":"2026-05-18T02:54:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1404.2188","created_at":"2026-05-18T02:54:38Z"},{"alias_kind":"pith_short_12","alias_value":"FDW5XTYHQ2M7","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_16","alias_value":"FDW5XTYHQ2M7KYG4","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_8","alias_value":"FDW5XTYH","created_at":"2026-05-18T12:28:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:FDW5XTYHQ2M7KYG4BKBRBRYPSG","target":"record","payload":{"canonical_record":{"source":{"id":"1404.2188","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2014-04-08T15:46:44Z","cross_cats_sorted":[],"title_canon_sha256":"50f96687d7e8da74164c8d608447706d9a55bc8d1fb6c6dc2beaf0d56f9630cb","abstract_canon_sha256":"06e01da8d5dfb1c0a6e276c76d65c847afa26e6fa7b30fa2b723d62dc3b21ec5"},"schema_version":"1.0"},"canonical_sha256":"28eddbcf078699f560dc0a8310c70f91af18fee458e28e4be4ceaf2d9c11674f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:54:38.286415Z","signature_b64":"25E32QpS+fab3B9QD5Vbcgie5fLk2Vo+AlxXnl37zaxxGvWZ6cm3EzP8yCNItylpoLZGHL6rOTxiR3kDJmlAAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"28eddbcf078699f560dc0a8310c70f91af18fee458e28e4be4ceaf2d9c11674f","last_reissued_at":"2026-05-18T02:54:38.285874Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:54:38.285874Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1404.2188","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":"2026-05-18T02:54:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z428btbRdZrqrcK1mx68KP5/gFSHAwFhecWGMgpzikjEmdPuSGvr8BSkvVVdBspuqOlPW82DlNUIqZhCQNUCCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T17:24:49.925443Z"},"content_sha256":"58c27c81d3939224263ff0e4a0159880e10f9dc9cb181e175ea98608eacb15ad","schema_version":"1.0","event_id":"sha256:58c27c81d3939224263ff0e4a0159880e10f9dc9cb181e175ea98608eacb15ad"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:FDW5XTYHQ2M7KYG4BKBRBRYPSG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Convolutional Neural Network for Modelling Sentences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Edward Grefenstette, Nal Kalchbrenner, Phil Blunsom","submitted_at":"2014-04-08T15:46:44Z","abstract_excerpt":"The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the DCNN in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1404.2188","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":"2026-05-18T02:54:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q/Bp3Zm3PQhzHwfzhQB4mDp2838YZbBOFQX58cxyYoWZuVgmI/C+2HUQ2oAHu7Lall3Atyr7KbP3LwzohnCGBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T17:24:49.926131Z"},"content_sha256":"843c3df102557b5c39c1b8a839b4e3957775c2f3ed34b252798e3bc5dc515bcb","schema_version":"1.0","event_id":"sha256:843c3df102557b5c39c1b8a839b4e3957775c2f3ed34b252798e3bc5dc515bcb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FDW5XTYHQ2M7KYG4BKBRBRYPSG/bundle.json","state_url":"https://pith.science/pith/FDW5XTYHQ2M7KYG4BKBRBRYPSG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FDW5XTYHQ2M7KYG4BKBRBRYPSG/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":"2026-05-24T17:24:49Z","links":{"resolver":"https://pith.science/pith/FDW5XTYHQ2M7KYG4BKBRBRYPSG","bundle":"https://pith.science/pith/FDW5XTYHQ2M7KYG4BKBRBRYPSG/bundle.json","state":"https://pith.science/pith/FDW5XTYHQ2M7KYG4BKBRBRYPSG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FDW5XTYHQ2M7KYG4BKBRBRYPSG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:FDW5XTYHQ2M7KYG4BKBRBRYPSG","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":"06e01da8d5dfb1c0a6e276c76d65c847afa26e6fa7b30fa2b723d62dc3b21ec5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2014-04-08T15:46:44Z","title_canon_sha256":"50f96687d7e8da74164c8d608447706d9a55bc8d1fb6c6dc2beaf0d56f9630cb"},"schema_version":"1.0","source":{"id":"1404.2188","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1404.2188","created_at":"2026-05-18T02:54:38Z"},{"alias_kind":"arxiv_version","alias_value":"1404.2188v1","created_at":"2026-05-18T02:54:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1404.2188","created_at":"2026-05-18T02:54:38Z"},{"alias_kind":"pith_short_12","alias_value":"FDW5XTYHQ2M7","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_16","alias_value":"FDW5XTYHQ2M7KYG4","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_8","alias_value":"FDW5XTYH","created_at":"2026-05-18T12:28:28Z"}],"graph_snapshots":[{"event_id":"sha256:843c3df102557b5c39c1b8a839b4e3957775c2f3ed34b252798e3bc5dc515bcb","target":"graph","created_at":"2026-05-18T02:54:38Z","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":"The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the DCNN in","authors_text":"Edward Grefenstette, Nal Kalchbrenner, Phil Blunsom","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2014-04-08T15:46:44Z","title":"A Convolutional Neural Network for Modelling Sentences"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1404.2188","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":"sha256:58c27c81d3939224263ff0e4a0159880e10f9dc9cb181e175ea98608eacb15ad","target":"record","created_at":"2026-05-18T02:54:38Z","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":"06e01da8d5dfb1c0a6e276c76d65c847afa26e6fa7b30fa2b723d62dc3b21ec5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2014-04-08T15:46:44Z","title_canon_sha256":"50f96687d7e8da74164c8d608447706d9a55bc8d1fb6c6dc2beaf0d56f9630cb"},"schema_version":"1.0","source":{"id":"1404.2188","kind":"arxiv","version":1}},"canonical_sha256":"28eddbcf078699f560dc0a8310c70f91af18fee458e28e4be4ceaf2d9c11674f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"28eddbcf078699f560dc0a8310c70f91af18fee458e28e4be4ceaf2d9c11674f","first_computed_at":"2026-05-18T02:54:38.285874Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:54:38.285874Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"25E32QpS+fab3B9QD5Vbcgie5fLk2Vo+AlxXnl37zaxxGvWZ6cm3EzP8yCNItylpoLZGHL6rOTxiR3kDJmlAAw==","signature_status":"signed_v1","signed_at":"2026-05-18T02:54:38.286415Z","signed_message":"canonical_sha256_bytes"},"source_id":"1404.2188","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:58c27c81d3939224263ff0e4a0159880e10f9dc9cb181e175ea98608eacb15ad","sha256:843c3df102557b5c39c1b8a839b4e3957775c2f3ed34b252798e3bc5dc515bcb"],"state_sha256":"970db158c40baff1283f973d6d4ea74d61f2b8dda24e1291eb48b86d0ac2b006"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZhTSSl2zbpMNivMR28FuzqnJLTnQarON+DDBfLrefAQ9XS9+pbHN6wXvC1andSQynifUTjqJWOqXdSUl0baFDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-24T17:24:49.929344Z","bundle_sha256":"a626fbc6d0a8698c1d5af586fc0805bbd01caa3e06c0a3082b2d508869612897"}}