{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:QMKMHAXG73UISZAXHFJI37DYTT","short_pith_number":"pith:QMKMHAXG","canonical_record":{"source":{"id":"1702.02098","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-02-07T16:58:18Z","cross_cats_sorted":[],"title_canon_sha256":"4a1474a10567d71467127bda0d2198ae0f801e81022e34bd88dd76fc2556def1","abstract_canon_sha256":"38d0649cfa1c3e9c88fd12c52c3f84cd4b0e98c5ab37aa035e7001074dbb8b4c"},"schema_version":"1.0"},"canonical_sha256":"8314c382e6fee889641739528dfc789cf6aa7099fd72de8c3398c20ff5e57d18","source":{"kind":"arxiv","id":"1702.02098","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.02098","created_at":"2026-05-18T00:39:46Z"},{"alias_kind":"arxiv_version","alias_value":"1702.02098v3","created_at":"2026-05-18T00:39:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.02098","created_at":"2026-05-18T00:39:46Z"},{"alias_kind":"pith_short_12","alias_value":"QMKMHAXG73UI","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"QMKMHAXG73UISZAX","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"QMKMHAXG","created_at":"2026-05-18T12:31:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:QMKMHAXG73UISZAXHFJI37DYTT","target":"record","payload":{"canonical_record":{"source":{"id":"1702.02098","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-02-07T16:58:18Z","cross_cats_sorted":[],"title_canon_sha256":"4a1474a10567d71467127bda0d2198ae0f801e81022e34bd88dd76fc2556def1","abstract_canon_sha256":"38d0649cfa1c3e9c88fd12c52c3f84cd4b0e98c5ab37aa035e7001074dbb8b4c"},"schema_version":"1.0"},"canonical_sha256":"8314c382e6fee889641739528dfc789cf6aa7099fd72de8c3398c20ff5e57d18","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:46.898872Z","signature_b64":"5QpPcq/YLUXTOHMaCvI1klUQtIvel19EkpDLlHsy/gcPEJqRFmYXmQVJ9OHA6G/SCC2QAC0zUi6ri/8hjVNUDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8314c382e6fee889641739528dfc789cf6aa7099fd72de8c3398c20ff5e57d18","last_reissued_at":"2026-05-18T00:39:46.898174Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:46.898174Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1702.02098","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":"2026-05-18T00:39:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LfXBoCdvFAVGypCZLgZonhfJUzLEQf84TBlvwDdcF6ud5bgorKnPJvTLrXqtKCjDUHktIUb/iwSUV35LAfHrAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T11:36:25.912548Z"},"content_sha256":"54914f1158f69e9e744728f725878020386f5b50aa8487f7e311c6797c987660","schema_version":"1.0","event_id":"sha256:54914f1158f69e9e744728f725878020386f5b50aa8487f7e311c6797c987660"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:QMKMHAXG73UISZAXHFJI37DYTT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fast and Accurate Entity Recognition with Iterated Dilated Convolutions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Andrew McCallum, David Belanger, Emma Strubell, Patrick Verga","submitted_at":"2017-02-07T16:58:18Z","abstract_excerpt":"Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs. Recent advances in GPU hardware have led to the emergence of bi-directional LSTMs as a standard method for obtaining per-token vector representations serving as input to labeling tasks such as NER (often followed by prediction in a linear-chain CRF). Though expressive and accurate, these models fail to fully exploit GPU parallelism, limiting their computational efficiency. This paper proposes a faster alternative to Bi-LSTMs for NER: Iterated Dil"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.02098","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":"2026-05-18T00:39:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cA6//szNUKJGoLYTcaJQQR3KLQgDQ+03oWG8gUcMXSadaWkL0D6x8r9Nlkw7Ue5xdPsfmBI02fwCzj11GPsxCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T11:36:25.913198Z"},"content_sha256":"01059486ce0d9f4be8a68da000937e5a0111e285fca10f79a00b8b89e9a74502","schema_version":"1.0","event_id":"sha256:01059486ce0d9f4be8a68da000937e5a0111e285fca10f79a00b8b89e9a74502"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QMKMHAXG73UISZAXHFJI37DYTT/bundle.json","state_url":"https://pith.science/pith/QMKMHAXG73UISZAXHFJI37DYTT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QMKMHAXG73UISZAXHFJI37DYTT/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":"2026-05-27T11:36:25Z","links":{"resolver":"https://pith.science/pith/QMKMHAXG73UISZAXHFJI37DYTT","bundle":"https://pith.science/pith/QMKMHAXG73UISZAXHFJI37DYTT/bundle.json","state":"https://pith.science/pith/QMKMHAXG73UISZAXHFJI37DYTT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QMKMHAXG73UISZAXHFJI37DYTT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:QMKMHAXG73UISZAXHFJI37DYTT","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":"38d0649cfa1c3e9c88fd12c52c3f84cd4b0e98c5ab37aa035e7001074dbb8b4c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-02-07T16:58:18Z","title_canon_sha256":"4a1474a10567d71467127bda0d2198ae0f801e81022e34bd88dd76fc2556def1"},"schema_version":"1.0","source":{"id":"1702.02098","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.02098","created_at":"2026-05-18T00:39:46Z"},{"alias_kind":"arxiv_version","alias_value":"1702.02098v3","created_at":"2026-05-18T00:39:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.02098","created_at":"2026-05-18T00:39:46Z"},{"alias_kind":"pith_short_12","alias_value":"QMKMHAXG73UI","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"QMKMHAXG73UISZAX","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"QMKMHAXG","created_at":"2026-05-18T12:31:39Z"}],"graph_snapshots":[{"event_id":"sha256:01059486ce0d9f4be8a68da000937e5a0111e285fca10f79a00b8b89e9a74502","target":"graph","created_at":"2026-05-18T00:39:46Z","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":"Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs. Recent advances in GPU hardware have led to the emergence of bi-directional LSTMs as a standard method for obtaining per-token vector representations serving as input to labeling tasks such as NER (often followed by prediction in a linear-chain CRF). Though expressive and accurate, these models fail to fully exploit GPU parallelism, limiting their computational efficiency. This paper proposes a faster alternative to Bi-LSTMs for NER: Iterated Dil","authors_text":"Andrew McCallum, David Belanger, Emma Strubell, Patrick Verga","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-02-07T16:58:18Z","title":"Fast and Accurate Entity Recognition with Iterated Dilated Convolutions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.02098","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":"sha256:54914f1158f69e9e744728f725878020386f5b50aa8487f7e311c6797c987660","target":"record","created_at":"2026-05-18T00:39:46Z","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":"38d0649cfa1c3e9c88fd12c52c3f84cd4b0e98c5ab37aa035e7001074dbb8b4c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-02-07T16:58:18Z","title_canon_sha256":"4a1474a10567d71467127bda0d2198ae0f801e81022e34bd88dd76fc2556def1"},"schema_version":"1.0","source":{"id":"1702.02098","kind":"arxiv","version":3}},"canonical_sha256":"8314c382e6fee889641739528dfc789cf6aa7099fd72de8c3398c20ff5e57d18","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8314c382e6fee889641739528dfc789cf6aa7099fd72de8c3398c20ff5e57d18","first_computed_at":"2026-05-18T00:39:46.898174Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:39:46.898174Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5QpPcq/YLUXTOHMaCvI1klUQtIvel19EkpDLlHsy/gcPEJqRFmYXmQVJ9OHA6G/SCC2QAC0zUi6ri/8hjVNUDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:39:46.898872Z","signed_message":"canonical_sha256_bytes"},"source_id":"1702.02098","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:54914f1158f69e9e744728f725878020386f5b50aa8487f7e311c6797c987660","sha256:01059486ce0d9f4be8a68da000937e5a0111e285fca10f79a00b8b89e9a74502"],"state_sha256":"a6a850fab3c939b504894eddc998e51e909bd637cb5019b776762fc69f2fe91f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kHzJqhMDiVWguHl/7unVGY9QSvfZUX0ECwbh72b0EDErz8R/oSRhLuCzlCrwmVFFYd/1QvZKMSTbdUSrG55yBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T11:36:25.916944Z","bundle_sha256":"5ae0b6a5e8f88ed598e9320d3a8e954fe56303bdeb7af3fb9a417bbdac744c32"}}