{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:5NMM2Z7THJEBP6AWYOLCBDEQRR","short_pith_number":"pith:5NMM2Z7T","schema_version":"1.0","canonical_sha256":"eb58cd67f33a4817f816c396208c908c6b3306ce94a09f93a2754451dc2f6c98","source":{"kind":"arxiv","id":"1906.07592","version":1},"attestation_state":"computed","paper":{"title":"Towards Robust Named Entity Recognition for Historic German","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Johannes Baiter, Stefan Schweter","submitted_at":"2019-06-18T14:06:40Z","abstract_excerpt":"Recent advances in language modeling using deep neural networks have shown that these models learn representations, that vary with the network depth from morphology to semantic relationships like co-reference. We apply pre-trained language models to low-resource named entity recognition for Historic German. We show on a series of experiments that character-based pre-trained language models do not run into trouble when faced with low-resource datasets. Our pre-trained character-based language models improve upon classical CRF-based methods and previous work on Bi-LSTMs by boosting F1 score perf"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1906.07592","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-18T14:06:40Z","cross_cats_sorted":[],"title_canon_sha256":"880440fa7e46a41a8cc85b00075825f247cac9fbe4469167f3046b26363f1e30","abstract_canon_sha256":"6453b15cb731e65c3bf2765ee3c72329644747605bae426920aeb9b695e69279"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:07.390257Z","signature_b64":"OKdyeXuxlJK6xi2ktOdd/I9L7kAvfvJJ13469UgntO1pVmUpDZtxKGT7EcKiiW5ebJPaH9u64Wge9QkYmZRaAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb58cd67f33a4817f816c396208c908c6b3306ce94a09f93a2754451dc2f6c98","last_reissued_at":"2026-05-17T23:43:07.389478Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:07.389478Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Robust Named Entity Recognition for Historic German","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Johannes Baiter, Stefan Schweter","submitted_at":"2019-06-18T14:06:40Z","abstract_excerpt":"Recent advances in language modeling using deep neural networks have shown that these models learn representations, that vary with the network depth from morphology to semantic relationships like co-reference. We apply pre-trained language models to low-resource named entity recognition for Historic German. We show on a series of experiments that character-based pre-trained language models do not run into trouble when faced with low-resource datasets. Our pre-trained character-based language models improve upon classical CRF-based methods and previous work on Bi-LSTMs by boosting F1 score perf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.07592","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1906.07592","created_at":"2026-05-17T23:43:07.389612+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.07592v1","created_at":"2026-05-17T23:43:07.389612+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.07592","created_at":"2026-05-17T23:43:07.389612+00:00"},{"alias_kind":"pith_short_12","alias_value":"5NMM2Z7THJEB","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"5NMM2Z7THJEBP6AW","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"5NMM2Z7T","created_at":"2026-05-18T12:33:10.108867+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5NMM2Z7THJEBP6AWYOLCBDEQRR","json":"https://pith.science/pith/5NMM2Z7THJEBP6AWYOLCBDEQRR.json","graph_json":"https://pith.science/api/pith-number/5NMM2Z7THJEBP6AWYOLCBDEQRR/graph.json","events_json":"https://pith.science/api/pith-number/5NMM2Z7THJEBP6AWYOLCBDEQRR/events.json","paper":"https://pith.science/paper/5NMM2Z7T"},"agent_actions":{"view_html":"https://pith.science/pith/5NMM2Z7THJEBP6AWYOLCBDEQRR","download_json":"https://pith.science/pith/5NMM2Z7THJEBP6AWYOLCBDEQRR.json","view_paper":"https://pith.science/paper/5NMM2Z7T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.07592&json=true","fetch_graph":"https://pith.science/api/pith-number/5NMM2Z7THJEBP6AWYOLCBDEQRR/graph.json","fetch_events":"https://pith.science/api/pith-number/5NMM2Z7THJEBP6AWYOLCBDEQRR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5NMM2Z7THJEBP6AWYOLCBDEQRR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5NMM2Z7THJEBP6AWYOLCBDEQRR/action/storage_attestation","attest_author":"https://pith.science/pith/5NMM2Z7THJEBP6AWYOLCBDEQRR/action/author_attestation","sign_citation":"https://pith.science/pith/5NMM2Z7THJEBP6AWYOLCBDEQRR/action/citation_signature","submit_replication":"https://pith.science/pith/5NMM2Z7THJEBP6AWYOLCBDEQRR/action/replication_record"}},"created_at":"2026-05-17T23:43:07.389612+00:00","updated_at":"2026-05-17T23:43:07.389612+00:00"}