{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:2DNQSKHUFZVFLKWEXY7VDPYQMQ","short_pith_number":"pith:2DNQSKHU","canonical_record":{"source":{"id":"1711.08231","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-11-22T11:18:31Z","cross_cats_sorted":[],"title_canon_sha256":"0f7b7f67a3c753f88808a4b4a35ed233bf7e9547f13169595facb783ace4c53f","abstract_canon_sha256":"16f02c90c72b2c9dc700cccbb97fde008d8fdab0e22ccbfda6dc8c935492b216"},"schema_version":"1.0"},"canonical_sha256":"d0db0928f42e6a55aac4be3f51bf1064014849288113c4f7d50d809780ad6812","source":{"kind":"arxiv","id":"1711.08231","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.08231","created_at":"2026-05-18T00:13:21Z"},{"alias_kind":"arxiv_version","alias_value":"1711.08231v3","created_at":"2026-05-18T00:13:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.08231","created_at":"2026-05-18T00:13:21Z"},{"alias_kind":"pith_short_12","alias_value":"2DNQSKHUFZVF","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_16","alias_value":"2DNQSKHUFZVFLKWE","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_8","alias_value":"2DNQSKHU","created_at":"2026-05-18T12:30:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:2DNQSKHUFZVFLKWEXY7VDPYQMQ","target":"record","payload":{"canonical_record":{"source":{"id":"1711.08231","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-11-22T11:18:31Z","cross_cats_sorted":[],"title_canon_sha256":"0f7b7f67a3c753f88808a4b4a35ed233bf7e9547f13169595facb783ace4c53f","abstract_canon_sha256":"16f02c90c72b2c9dc700cccbb97fde008d8fdab0e22ccbfda6dc8c935492b216"},"schema_version":"1.0"},"canonical_sha256":"d0db0928f42e6a55aac4be3f51bf1064014849288113c4f7d50d809780ad6812","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:21.743654Z","signature_b64":"DwWE/Ufe292dH5sxlR/ZRDlCmdYxvIVibgqCz0t5VDkkAEVsWYSVqa0OUwkTDbJ6jUurw2KA2NRxh9lAFH5/Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0db0928f42e6a55aac4be3f51bf1064014849288113c4f7d50d809780ad6812","last_reissued_at":"2026-05-18T00:13:21.743005Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:21.743005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.08231","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:13:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7cGtw6oLfitS5OB6a6CsPEL4Yq6CT+rRHmemCBeEgbHXs8Va20NV+ZvJT7iyZXY30spexTACeq3Uyq1HwEHmCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T11:34:08.647029Z"},"content_sha256":"d1a2f28d19e0c3d1c7a6e8475f29ae50b95bbd1bc2e4dba6500bd698f66363ec","schema_version":"1.0","event_id":"sha256:d1a2f28d19e0c3d1c7a6e8475f29ae50b95bbd1bc2e4dba6500bd698f66363ec"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:2DNQSKHUFZVFLKWEXY7VDPYQMQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Does Higher Order LSTM Have Better Accuracy for Segmenting and Labeling Sequence Data?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Shuming Ma, Xuancheng Ren, Xu Sun, Yang Yang, Yi Zhang","submitted_at":"2017-11-22T11:18:31Z","abstract_excerpt":"Existing neural models usually predict the tag of the current token independent of the neighboring tags. The popular LSTM-CRF model considers the tag dependencies between every two consecutive tags. However, it is hard for existing neural models to take longer distance dependencies of tags into consideration. The scalability is mainly limited by the complex model structures and the cost of dynamic programming during training. In our work, we first design a new model called \"high order LSTM\" to predict multiple tags for the current token which contains not only the current tag but also the prev"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.08231","kind":"arxiv","version":3},"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":"2026-05-18T00:13:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oE6IZHjeLWohCal5qyJAx1kE/sWA/eBHDctyuha8loUp8UgKqZoHZbHOr+8/uexf0kI6VUL0Kqpd8jgbYjZMAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T11:34:08.647392Z"},"content_sha256":"85eb3c83e21232580dc16bc46d1987d89869defe6854b10edccfa8ac514e0b7c","schema_version":"1.0","event_id":"sha256:85eb3c83e21232580dc16bc46d1987d89869defe6854b10edccfa8ac514e0b7c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2DNQSKHUFZVFLKWEXY7VDPYQMQ/bundle.json","state_url":"https://pith.science/pith/2DNQSKHUFZVFLKWEXY7VDPYQMQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2DNQSKHUFZVFLKWEXY7VDPYQMQ/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":"2026-06-02T11:34:08Z","links":{"resolver":"https://pith.science/pith/2DNQSKHUFZVFLKWEXY7VDPYQMQ","bundle":"https://pith.science/pith/2DNQSKHUFZVFLKWEXY7VDPYQMQ/bundle.json","state":"https://pith.science/pith/2DNQSKHUFZVFLKWEXY7VDPYQMQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2DNQSKHUFZVFLKWEXY7VDPYQMQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:2DNQSKHUFZVFLKWEXY7VDPYQMQ","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":"16f02c90c72b2c9dc700cccbb97fde008d8fdab0e22ccbfda6dc8c935492b216","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-11-22T11:18:31Z","title_canon_sha256":"0f7b7f67a3c753f88808a4b4a35ed233bf7e9547f13169595facb783ace4c53f"},"schema_version":"1.0","source":{"id":"1711.08231","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.08231","created_at":"2026-05-18T00:13:21Z"},{"alias_kind":"arxiv_version","alias_value":"1711.08231v3","created_at":"2026-05-18T00:13:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.08231","created_at":"2026-05-18T00:13:21Z"},{"alias_kind":"pith_short_12","alias_value":"2DNQSKHUFZVF","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_16","alias_value":"2DNQSKHUFZVFLKWE","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_8","alias_value":"2DNQSKHU","created_at":"2026-05-18T12:30:55Z"}],"graph_snapshots":[{"event_id":"sha256:85eb3c83e21232580dc16bc46d1987d89869defe6854b10edccfa8ac514e0b7c","target":"graph","created_at":"2026-05-18T00:13:21Z","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":"Existing neural models usually predict the tag of the current token independent of the neighboring tags. The popular LSTM-CRF model considers the tag dependencies between every two consecutive tags. However, it is hard for existing neural models to take longer distance dependencies of tags into consideration. The scalability is mainly limited by the complex model structures and the cost of dynamic programming during training. In our work, we first design a new model called \"high order LSTM\" to predict multiple tags for the current token which contains not only the current tag but also the prev","authors_text":"Shuming Ma, Xuancheng Ren, Xu Sun, Yang Yang, Yi Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-11-22T11:18:31Z","title":"Does Higher Order LSTM Have Better Accuracy for Segmenting and Labeling Sequence Data?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.08231","kind":"arxiv","version":3},"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:d1a2f28d19e0c3d1c7a6e8475f29ae50b95bbd1bc2e4dba6500bd698f66363ec","target":"record","created_at":"2026-05-18T00:13:21Z","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":"16f02c90c72b2c9dc700cccbb97fde008d8fdab0e22ccbfda6dc8c935492b216","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-11-22T11:18:31Z","title_canon_sha256":"0f7b7f67a3c753f88808a4b4a35ed233bf7e9547f13169595facb783ace4c53f"},"schema_version":"1.0","source":{"id":"1711.08231","kind":"arxiv","version":3}},"canonical_sha256":"d0db0928f42e6a55aac4be3f51bf1064014849288113c4f7d50d809780ad6812","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d0db0928f42e6a55aac4be3f51bf1064014849288113c4f7d50d809780ad6812","first_computed_at":"2026-05-18T00:13:21.743005Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:13:21.743005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DwWE/Ufe292dH5sxlR/ZRDlCmdYxvIVibgqCz0t5VDkkAEVsWYSVqa0OUwkTDbJ6jUurw2KA2NRxh9lAFH5/Cg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:13:21.743654Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.08231","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d1a2f28d19e0c3d1c7a6e8475f29ae50b95bbd1bc2e4dba6500bd698f66363ec","sha256:85eb3c83e21232580dc16bc46d1987d89869defe6854b10edccfa8ac514e0b7c"],"state_sha256":"11ba20172f572c72dd3436a4c28ac9938f6a1a4b268520ddd00813107931a0b9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AzbmnYp9Ogc7yd2rXb8cl8PwPDFxX8eb+aGsF8Mw80eU0JW3j9RvEy7AKHgjx5lKDodrHEGMh8JWIbCWOsJNDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T11:34:08.649261Z","bundle_sha256":"bfc7c9aa8b04c54e4ddfd2c2fe249de5d8b5589ecd9b0b8e0b78bbcb7d8618bc"}}