{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:RRSAGPPRBXT46BYYJM34TU6PJG","short_pith_number":"pith:RRSAGPPR","canonical_record":{"source":{"id":"1606.06406","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-06-21T03:20:59Z","cross_cats_sorted":[],"title_canon_sha256":"5cd44d0f3ae3cda9c7c3d07020162a53773915c1f97b45b081753562fef8baa1","abstract_canon_sha256":"5881feb9f37da105523bfaa275a56a8a10d04c3afc6039c29e32a89e941b386b"},"schema_version":"1.0"},"canonical_sha256":"8c64033df10de7cf07184b37c9d3cf49b32ec4a789a8f3502996f370ae1b2c3f","source":{"kind":"arxiv","id":"1606.06406","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.06406","created_at":"2026-05-18T01:12:09Z"},{"alias_kind":"arxiv_version","alias_value":"1606.06406v1","created_at":"2026-05-18T01:12:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.06406","created_at":"2026-05-18T01:12:09Z"},{"alias_kind":"pith_short_12","alias_value":"RRSAGPPRBXT4","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_16","alias_value":"RRSAGPPRBXT46BYY","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_8","alias_value":"RRSAGPPR","created_at":"2026-05-18T12:30:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:RRSAGPPRBXT46BYYJM34TU6PJG","target":"record","payload":{"canonical_record":{"source":{"id":"1606.06406","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-06-21T03:20:59Z","cross_cats_sorted":[],"title_canon_sha256":"5cd44d0f3ae3cda9c7c3d07020162a53773915c1f97b45b081753562fef8baa1","abstract_canon_sha256":"5881feb9f37da105523bfaa275a56a8a10d04c3afc6039c29e32a89e941b386b"},"schema_version":"1.0"},"canonical_sha256":"8c64033df10de7cf07184b37c9d3cf49b32ec4a789a8f3502996f370ae1b2c3f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:12:09.669661Z","signature_b64":"n/8HnKjsyhCVGMT+uPdkwea9W6cB694TQBrNLOxkuBM4BjxHQJJw/Ts8e+k7VG4endtxw7xhQ1Bsfdnay73UAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8c64033df10de7cf07184b37c9d3cf49b32ec4a789a8f3502996f370ae1b2c3f","last_reissued_at":"2026-05-18T01:12:09.669112Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:12:09.669112Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1606.06406","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":"2026-05-18T01:12:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YXdxP4dLcmEt+3qSEZ02m/q54cd91W+PAXSj7k3kByZRiX9UI+8E9TEiL8V8B2eTP2hYaL7pXH33lE1KPB+/Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T05:37:36.301847Z"},"content_sha256":"1295138b6372094e01a000ed9a223d9fd2ccde698f347c374201e7f2fc66260f","schema_version":"1.0","event_id":"sha256:1295138b6372094e01a000ed9a223d9fd2ccde698f347c374201e7f2fc66260f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:RRSAGPPRBXT46BYYJM34TU6PJG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Incremental Parsing with Minimal Features Using Bi-Directional LSTM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"James Cross, Liang Huang","submitted_at":"2016-06-21T03:20:59Z","abstract_excerpt":"Recently, neural network approaches for parsing have largely automated the combination of individual features, but still rely on (often a larger number of) atomic features created from human linguistic intuition, and potentially omitting important global context. To further reduce feature engineering to the bare minimum, we use bi-directional LSTM sentence representations to model a parser state with only three sentence positions, which automatically identifies important aspects of the entire sentence. This model achieves state-of-the-art results among greedy dependency parsers for English. We"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.06406","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":"2026-05-18T01:12:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o7QsRNwQ5+dfiXz7+MM5tUcd89hz6eH0j/pYHGDFF3c3GXzfqy5eNJu8xkLorJZepkgJOmMMURyj/FRhK8BiDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T05:37:36.302190Z"},"content_sha256":"2e53b86c33cacaf8c7595aba0ece4b55a7e91e18b0a5ab3c1a9d114ea936451d","schema_version":"1.0","event_id":"sha256:2e53b86c33cacaf8c7595aba0ece4b55a7e91e18b0a5ab3c1a9d114ea936451d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RRSAGPPRBXT46BYYJM34TU6PJG/bundle.json","state_url":"https://pith.science/pith/RRSAGPPRBXT46BYYJM34TU6PJG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RRSAGPPRBXT46BYYJM34TU6PJG/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":"2026-07-02T05:37:36Z","links":{"resolver":"https://pith.science/pith/RRSAGPPRBXT46BYYJM34TU6PJG","bundle":"https://pith.science/pith/RRSAGPPRBXT46BYYJM34TU6PJG/bundle.json","state":"https://pith.science/pith/RRSAGPPRBXT46BYYJM34TU6PJG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RRSAGPPRBXT46BYYJM34TU6PJG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:RRSAGPPRBXT46BYYJM34TU6PJG","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":"5881feb9f37da105523bfaa275a56a8a10d04c3afc6039c29e32a89e941b386b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-06-21T03:20:59Z","title_canon_sha256":"5cd44d0f3ae3cda9c7c3d07020162a53773915c1f97b45b081753562fef8baa1"},"schema_version":"1.0","source":{"id":"1606.06406","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.06406","created_at":"2026-05-18T01:12:09Z"},{"alias_kind":"arxiv_version","alias_value":"1606.06406v1","created_at":"2026-05-18T01:12:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.06406","created_at":"2026-05-18T01:12:09Z"},{"alias_kind":"pith_short_12","alias_value":"RRSAGPPRBXT4","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_16","alias_value":"RRSAGPPRBXT46BYY","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_8","alias_value":"RRSAGPPR","created_at":"2026-05-18T12:30:41Z"}],"graph_snapshots":[{"event_id":"sha256:2e53b86c33cacaf8c7595aba0ece4b55a7e91e18b0a5ab3c1a9d114ea936451d","target":"graph","created_at":"2026-05-18T01:12:09Z","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":"Recently, neural network approaches for parsing have largely automated the combination of individual features, but still rely on (often a larger number of) atomic features created from human linguistic intuition, and potentially omitting important global context. To further reduce feature engineering to the bare minimum, we use bi-directional LSTM sentence representations to model a parser state with only three sentence positions, which automatically identifies important aspects of the entire sentence. This model achieves state-of-the-art results among greedy dependency parsers for English. We","authors_text":"James Cross, Liang Huang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-06-21T03:20:59Z","title":"Incremental Parsing with Minimal Features Using Bi-Directional LSTM"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.06406","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":"sha256:1295138b6372094e01a000ed9a223d9fd2ccde698f347c374201e7f2fc66260f","target":"record","created_at":"2026-05-18T01:12:09Z","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":"5881feb9f37da105523bfaa275a56a8a10d04c3afc6039c29e32a89e941b386b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-06-21T03:20:59Z","title_canon_sha256":"5cd44d0f3ae3cda9c7c3d07020162a53773915c1f97b45b081753562fef8baa1"},"schema_version":"1.0","source":{"id":"1606.06406","kind":"arxiv","version":1}},"canonical_sha256":"8c64033df10de7cf07184b37c9d3cf49b32ec4a789a8f3502996f370ae1b2c3f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8c64033df10de7cf07184b37c9d3cf49b32ec4a789a8f3502996f370ae1b2c3f","first_computed_at":"2026-05-18T01:12:09.669112Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:12:09.669112Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"n/8HnKjsyhCVGMT+uPdkwea9W6cB694TQBrNLOxkuBM4BjxHQJJw/Ts8e+k7VG4endtxw7xhQ1Bsfdnay73UAw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:12:09.669661Z","signed_message":"canonical_sha256_bytes"},"source_id":"1606.06406","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1295138b6372094e01a000ed9a223d9fd2ccde698f347c374201e7f2fc66260f","sha256:2e53b86c33cacaf8c7595aba0ece4b55a7e91e18b0a5ab3c1a9d114ea936451d"],"state_sha256":"03086f9448ca6bc2cbb678c167b537932db3b90eb0977c773f15699e8589cbc5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Jwa7eo01h/SjRlBZA/ecxPpYQsaoM7BcUaE6hpEkoXuFLn4eZjdKRgd1XblJVVcp6F8bGTFlzMCM+ZHq4gHQCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-02T05:37:36.304145Z","bundle_sha256":"8ff420330ea7a798b6c6c55a9a5bd084bec013a32087dcffe1751483f0621070"}}