{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:QX4NG7LVVCKO4MBTWB32XAEGBR","short_pith_number":"pith:QX4NG7LV","canonical_record":{"source":{"id":"1508.01991","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-08-09T06:32:47Z","cross_cats_sorted":[],"title_canon_sha256":"285b91c2b7668300615e7210b37c52a088e63b3e1c3d42c8f83fc563a21a86ad","abstract_canon_sha256":"f814f6df7db36af271ba545aa103cd41c64c94c8e9b9aec930549bdd0faf4690"},"schema_version":"1.0"},"canonical_sha256":"85f8d37d75a894ee3033b077ab80860c445bf1637f3cb38ab5c03cf145b8c756","source":{"kind":"arxiv","id":"1508.01991","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1508.01991","created_at":"2026-05-18T01:35:37Z"},{"alias_kind":"arxiv_version","alias_value":"1508.01991v1","created_at":"2026-05-18T01:35:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1508.01991","created_at":"2026-05-18T01:35:37Z"},{"alias_kind":"pith_short_12","alias_value":"QX4NG7LVVCKO","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_16","alias_value":"QX4NG7LVVCKO4MBT","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_8","alias_value":"QX4NG7LV","created_at":"2026-05-18T12:29:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:QX4NG7LVVCKO4MBTWB32XAEGBR","target":"record","payload":{"canonical_record":{"source":{"id":"1508.01991","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-08-09T06:32:47Z","cross_cats_sorted":[],"title_canon_sha256":"285b91c2b7668300615e7210b37c52a088e63b3e1c3d42c8f83fc563a21a86ad","abstract_canon_sha256":"f814f6df7db36af271ba545aa103cd41c64c94c8e9b9aec930549bdd0faf4690"},"schema_version":"1.0"},"canonical_sha256":"85f8d37d75a894ee3033b077ab80860c445bf1637f3cb38ab5c03cf145b8c756","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:35:37.379308Z","signature_b64":"kfbbDwxeBgaor+aY8csnNc4xkqD8phKlxxLtFztqe5OcDzSWOTNdBZNeaNtknLh6pY9b1G4O/OvA6yPGdIldDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"85f8d37d75a894ee3033b077ab80860c445bf1637f3cb38ab5c03cf145b8c756","last_reissued_at":"2026-05-18T01:35:37.378693Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:35:37.378693Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1508.01991","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":"2026-05-18T01:35:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ER9BLQN1q8+EKgL7Dgfegwy/VZ1Oca/xikT7uyo7k6+Lq6XFH3MJ9HGLOGT7FbVduH+pPP14ZgfKGE46S2yBCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T16:25:05.186253Z"},"content_sha256":"a31b6d5220a45c6766d1b35ac41bd754d349dc00c9cf43db3cea322625335a1c","schema_version":"1.0","event_id":"sha256:a31b6d5220a45c6766d1b35ac41bd754d349dc00c9cf43db3cea322625335a1c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:QX4NG7LVVCKO4MBTWB32XAEGBR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bidirectional LSTM-CRF Models for Sequence Tagging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Kai Yu, Wei Xu, Zhiheng Huang","submitted_at":"2015-08-09T06:32:47Z","abstract_excerpt":"In this paper, we propose a variety of Long Short-Term Memory (LSTM) based models for sequence tagging. These models include LSTM networks, bidirectional LSTM (BI-LSTM) networks, LSTM with a Conditional Random Field (CRF) layer (LSTM-CRF) and bidirectional LSTM with a CRF layer (BI-LSTM-CRF). Our work is the first to apply a bidirectional LSTM CRF (denoted as BI-LSTM-CRF) model to NLP benchmark sequence tagging data sets. We show that the BI-LSTM-CRF model can efficiently use both past and future input features thanks to a bidirectional LSTM component. It can also use sentence level tag inform"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1508.01991","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":"2026-05-18T01:35:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gqgxxbwhUF9H3wobyz3eN24ucsmGxa6L7SAuEvCZOMNIlrrqe6nR/+I8DnAPIdbixKTs+i3KmTeUEpCWlLaBAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T16:25:05.186617Z"},"content_sha256":"5d051bab7de29673998423291c9f98f8557cc96da503d1e4ddd8f7150dd33902","schema_version":"1.0","event_id":"sha256:5d051bab7de29673998423291c9f98f8557cc96da503d1e4ddd8f7150dd33902"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QX4NG7LVVCKO4MBTWB32XAEGBR/bundle.json","state_url":"https://pith.science/pith/QX4NG7LVVCKO4MBTWB32XAEGBR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QX4NG7LVVCKO4MBTWB32XAEGBR/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":"2026-05-27T16:25:05Z","links":{"resolver":"https://pith.science/pith/QX4NG7LVVCKO4MBTWB32XAEGBR","bundle":"https://pith.science/pith/QX4NG7LVVCKO4MBTWB32XAEGBR/bundle.json","state":"https://pith.science/pith/QX4NG7LVVCKO4MBTWB32XAEGBR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QX4NG7LVVCKO4MBTWB32XAEGBR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:QX4NG7LVVCKO4MBTWB32XAEGBR","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":"f814f6df7db36af271ba545aa103cd41c64c94c8e9b9aec930549bdd0faf4690","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-08-09T06:32:47Z","title_canon_sha256":"285b91c2b7668300615e7210b37c52a088e63b3e1c3d42c8f83fc563a21a86ad"},"schema_version":"1.0","source":{"id":"1508.01991","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1508.01991","created_at":"2026-05-18T01:35:37Z"},{"alias_kind":"arxiv_version","alias_value":"1508.01991v1","created_at":"2026-05-18T01:35:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1508.01991","created_at":"2026-05-18T01:35:37Z"},{"alias_kind":"pith_short_12","alias_value":"QX4NG7LVVCKO","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_16","alias_value":"QX4NG7LVVCKO4MBT","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_8","alias_value":"QX4NG7LV","created_at":"2026-05-18T12:29:39Z"}],"graph_snapshots":[{"event_id":"sha256:5d051bab7de29673998423291c9f98f8557cc96da503d1e4ddd8f7150dd33902","target":"graph","created_at":"2026-05-18T01:35:37Z","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 this paper, we propose a variety of Long Short-Term Memory (LSTM) based models for sequence tagging. These models include LSTM networks, bidirectional LSTM (BI-LSTM) networks, LSTM with a Conditional Random Field (CRF) layer (LSTM-CRF) and bidirectional LSTM with a CRF layer (BI-LSTM-CRF). Our work is the first to apply a bidirectional LSTM CRF (denoted as BI-LSTM-CRF) model to NLP benchmark sequence tagging data sets. We show that the BI-LSTM-CRF model can efficiently use both past and future input features thanks to a bidirectional LSTM component. It can also use sentence level tag inform","authors_text":"Kai Yu, Wei Xu, Zhiheng Huang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-08-09T06:32:47Z","title":"Bidirectional LSTM-CRF Models for Sequence Tagging"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1508.01991","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:a31b6d5220a45c6766d1b35ac41bd754d349dc00c9cf43db3cea322625335a1c","target":"record","created_at":"2026-05-18T01:35:37Z","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":"f814f6df7db36af271ba545aa103cd41c64c94c8e9b9aec930549bdd0faf4690","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-08-09T06:32:47Z","title_canon_sha256":"285b91c2b7668300615e7210b37c52a088e63b3e1c3d42c8f83fc563a21a86ad"},"schema_version":"1.0","source":{"id":"1508.01991","kind":"arxiv","version":1}},"canonical_sha256":"85f8d37d75a894ee3033b077ab80860c445bf1637f3cb38ab5c03cf145b8c756","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"85f8d37d75a894ee3033b077ab80860c445bf1637f3cb38ab5c03cf145b8c756","first_computed_at":"2026-05-18T01:35:37.378693Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:35:37.378693Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kfbbDwxeBgaor+aY8csnNc4xkqD8phKlxxLtFztqe5OcDzSWOTNdBZNeaNtknLh6pY9b1G4O/OvA6yPGdIldDg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:35:37.379308Z","signed_message":"canonical_sha256_bytes"},"source_id":"1508.01991","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a31b6d5220a45c6766d1b35ac41bd754d349dc00c9cf43db3cea322625335a1c","sha256:5d051bab7de29673998423291c9f98f8557cc96da503d1e4ddd8f7150dd33902"],"state_sha256":"0667c0a2ac88df634cb80932aa3f495f0934a4e5e4a38586661ba784bb162ff7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4PRJUVXG1G6y9XD3H5ogo9dX6nQVsiTT1CZnwiMuTs8YWN5UI+4iN69dadev1zU28dFiAjOc5Gl5iVKGdXD6BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T16:25:05.189423Z","bundle_sha256":"1d237832c689531d9378d4668990dd2e88645afecdb15cafcc994d86f7c1265e"}}