{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:BRZR4PLCQW2Y77QPADLEFKTDR6","short_pith_number":"pith:BRZR4PLC","canonical_record":{"source":{"id":"1707.05928","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-07-19T03:18:40Z","cross_cats_sorted":[],"title_canon_sha256":"62f8ad9a35e7ddd5d0ca1f72e1286ad2aec2778644b226330533a30be70eeeba","abstract_canon_sha256":"2ed5d240b64fde80a2cd7b1a3776d9d8729c5e5b20e9f7a7b7ccbda3ed3bfb71"},"schema_version":"1.0"},"canonical_sha256":"0c731e3d6285b58ffe0f00d642aa638fa3cc22b20ad823154bcaf96b8d2c10a9","source":{"kind":"arxiv","id":"1707.05928","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.05928","created_at":"2026-05-18T00:24:31Z"},{"alias_kind":"arxiv_version","alias_value":"1707.05928v3","created_at":"2026-05-18T00:24:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.05928","created_at":"2026-05-18T00:24:31Z"},{"alias_kind":"pith_short_12","alias_value":"BRZR4PLCQW2Y","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"BRZR4PLCQW2Y77QP","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"BRZR4PLC","created_at":"2026-05-18T12:31:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:BRZR4PLCQW2Y77QPADLEFKTDR6","target":"record","payload":{"canonical_record":{"source":{"id":"1707.05928","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-07-19T03:18:40Z","cross_cats_sorted":[],"title_canon_sha256":"62f8ad9a35e7ddd5d0ca1f72e1286ad2aec2778644b226330533a30be70eeeba","abstract_canon_sha256":"2ed5d240b64fde80a2cd7b1a3776d9d8729c5e5b20e9f7a7b7ccbda3ed3bfb71"},"schema_version":"1.0"},"canonical_sha256":"0c731e3d6285b58ffe0f00d642aa638fa3cc22b20ad823154bcaf96b8d2c10a9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:31.067807Z","signature_b64":"gl9tQtFlE1OKunzg3KjBoZs/wizVKODqBVNFr7TyEPyaDjTNoXovrTvPxtp3RZB3Vjqjw1hSROoijfYVLS8fCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c731e3d6285b58ffe0f00d642aa638fa3cc22b20ad823154bcaf96b8d2c10a9","last_reissued_at":"2026-05-18T00:24:31.067194Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:31.067194Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1707.05928","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":"2026-05-18T00:24:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7p97MOTGcBmAQKL45ztMWNy3SUFFuSz8VOwFgLc1EpqvP7qyM8PKkAKXQhUrMuN4I/TrFJcA7E1pf6aUVWF6Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T07:27:32.145228Z"},"content_sha256":"ecc410c29125f6470b3ae97f40bda1d6ffc8c9c79dba9e58d15c6b1f55518ebb","schema_version":"1.0","event_id":"sha256:ecc410c29125f6470b3ae97f40bda1d6ffc8c9c79dba9e58d15c6b1f55518ebb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:BRZR4PLCQW2Y77QPADLEFKTDR6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Active Learning for Named Entity Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Animashree Anandkumar, Hyokun Yun, Yakov Kronrod, Yanyao Shen, Zachary C. Lipton","submitted_at":"2017-07-19T03:18:40Z","abstract_excerpt":"Deep learning has yielded state-of-the-art performance on many natural language processing tasks including named entity recognition (NER). However, this typically requires large amounts of labeled data. In this work, we demonstrate that the amount of labeled training data can be drastically reduced when deep learning is combined with active learning. While active learning is sample-efficient, it can be computationally expensive since it requires iterative retraining. To speed this up, we introduce a lightweight architecture for NER, viz., the CNN-CNN-LSTM model consisting of convolutional char"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.05928","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":"2026-05-18T00:24:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"izC3z6bLctl/OWWquydYL7kYYZgQ5IrliSdRQWMxOBRE3Y0jmDdW10qqb7jnBWsxMtd9e/q4YYKVC/IS2LjbAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T07:27:32.145904Z"},"content_sha256":"25840e869976f624ac13ff4716c25e79faa120853f2cddaae19ff29c7f7d2c53","schema_version":"1.0","event_id":"sha256:25840e869976f624ac13ff4716c25e79faa120853f2cddaae19ff29c7f7d2c53"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BRZR4PLCQW2Y77QPADLEFKTDR6/bundle.json","state_url":"https://pith.science/pith/BRZR4PLCQW2Y77QPADLEFKTDR6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BRZR4PLCQW2Y77QPADLEFKTDR6/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":"2026-05-27T07:27:32Z","links":{"resolver":"https://pith.science/pith/BRZR4PLCQW2Y77QPADLEFKTDR6","bundle":"https://pith.science/pith/BRZR4PLCQW2Y77QPADLEFKTDR6/bundle.json","state":"https://pith.science/pith/BRZR4PLCQW2Y77QPADLEFKTDR6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BRZR4PLCQW2Y77QPADLEFKTDR6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:BRZR4PLCQW2Y77QPADLEFKTDR6","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":"2ed5d240b64fde80a2cd7b1a3776d9d8729c5e5b20e9f7a7b7ccbda3ed3bfb71","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-07-19T03:18:40Z","title_canon_sha256":"62f8ad9a35e7ddd5d0ca1f72e1286ad2aec2778644b226330533a30be70eeeba"},"schema_version":"1.0","source":{"id":"1707.05928","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.05928","created_at":"2026-05-18T00:24:31Z"},{"alias_kind":"arxiv_version","alias_value":"1707.05928v3","created_at":"2026-05-18T00:24:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.05928","created_at":"2026-05-18T00:24:31Z"},{"alias_kind":"pith_short_12","alias_value":"BRZR4PLCQW2Y","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"BRZR4PLCQW2Y77QP","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"BRZR4PLC","created_at":"2026-05-18T12:31:08Z"}],"graph_snapshots":[{"event_id":"sha256:25840e869976f624ac13ff4716c25e79faa120853f2cddaae19ff29c7f7d2c53","target":"graph","created_at":"2026-05-18T00:24:31Z","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":"Deep learning has yielded state-of-the-art performance on many natural language processing tasks including named entity recognition (NER). However, this typically requires large amounts of labeled data. In this work, we demonstrate that the amount of labeled training data can be drastically reduced when deep learning is combined with active learning. While active learning is sample-efficient, it can be computationally expensive since it requires iterative retraining. To speed this up, we introduce a lightweight architecture for NER, viz., the CNN-CNN-LSTM model consisting of convolutional char","authors_text":"Animashree Anandkumar, Hyokun Yun, Yakov Kronrod, Yanyao Shen, Zachary C. Lipton","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-07-19T03:18:40Z","title":"Deep Active Learning for Named Entity Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.05928","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:ecc410c29125f6470b3ae97f40bda1d6ffc8c9c79dba9e58d15c6b1f55518ebb","target":"record","created_at":"2026-05-18T00:24:31Z","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":"2ed5d240b64fde80a2cd7b1a3776d9d8729c5e5b20e9f7a7b7ccbda3ed3bfb71","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-07-19T03:18:40Z","title_canon_sha256":"62f8ad9a35e7ddd5d0ca1f72e1286ad2aec2778644b226330533a30be70eeeba"},"schema_version":"1.0","source":{"id":"1707.05928","kind":"arxiv","version":3}},"canonical_sha256":"0c731e3d6285b58ffe0f00d642aa638fa3cc22b20ad823154bcaf96b8d2c10a9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0c731e3d6285b58ffe0f00d642aa638fa3cc22b20ad823154bcaf96b8d2c10a9","first_computed_at":"2026-05-18T00:24:31.067194Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:24:31.067194Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gl9tQtFlE1OKunzg3KjBoZs/wizVKODqBVNFr7TyEPyaDjTNoXovrTvPxtp3RZB3Vjqjw1hSROoijfYVLS8fCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:24:31.067807Z","signed_message":"canonical_sha256_bytes"},"source_id":"1707.05928","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ecc410c29125f6470b3ae97f40bda1d6ffc8c9c79dba9e58d15c6b1f55518ebb","sha256:25840e869976f624ac13ff4716c25e79faa120853f2cddaae19ff29c7f7d2c53"],"state_sha256":"591aa0ca09f5f61923d9f4dc641feea92e2b33266537012a736318ff29278594"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u07pYDGZie6IF9XeGf54mOlC+PY5bczXjFJP/8faTpGQV7xkZ6U30JZMevyKm/ot6UqDw6arKPHQOW/ghZWQDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T07:27:32.149914Z","bundle_sha256":"73cebebdc0fae929ff6b23cdb8400ec71f86cc236b374b47a087560e4fb8d773"}}