{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:OYM5P4IO327EQPYGRIJX62EJIN","short_pith_number":"pith:OYM5P4IO","schema_version":"1.0","canonical_sha256":"7619d7f10edebe483f068a137f6889434398d83bdc6d9534645fe380a3b99ae8","source":{"kind":"arxiv","id":"1709.04109","version":4},"attestation_state":"computed","paper":{"title":"Empower Sequence Labeling with Task-Aware Neural Language Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Frank F. Xu, Huan Gui, Jian Peng, Jiawei Han, Jingbo Shang, Liyuan Liu, Xiang Ren","submitted_at":"2017-09-13T02:13:25Z","abstract_excerpt":"Linguistic sequence labeling is a general modeling approach that encompasses a variety of problems, such as part-of-speech tagging and named entity recognition. Recent advances in neural networks (NNs) make it possible to build reliable models without handcrafted features. However, in many cases, it is hard to obtain sufficient annotations to train these models. In this study, we develop a novel neural framework to extract abundant knowledge hidden in raw texts to empower the sequence labeling task. Besides word-level knowledge contained in pre-trained word embeddings, character-aware neural l"},"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":"1709.04109","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-09-13T02:13:25Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"3fa4367376900f017a78976ef071b60a20d6d222541b246881dd14a57cf928b0","abstract_canon_sha256":"75873d56df587555d290fc08b290dad435df13e60ebe4043a2a68664e1861279"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:29:45.569178Z","signature_b64":"54zAmYkxqpmIxDDGBa6p9GighTAUxM/MPVjNxHy50boYWQFp6V3F+q8J/yl6KwZWbLr4z42lBJvKG7hZsjvWBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7619d7f10edebe483f068a137f6889434398d83bdc6d9534645fe380a3b99ae8","last_reissued_at":"2026-05-18T00:29:45.568714Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:29:45.568714Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Empower Sequence Labeling with Task-Aware Neural Language Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Frank F. Xu, Huan Gui, Jian Peng, Jiawei Han, Jingbo Shang, Liyuan Liu, Xiang Ren","submitted_at":"2017-09-13T02:13:25Z","abstract_excerpt":"Linguistic sequence labeling is a general modeling approach that encompasses a variety of problems, such as part-of-speech tagging and named entity recognition. Recent advances in neural networks (NNs) make it possible to build reliable models without handcrafted features. However, in many cases, it is hard to obtain sufficient annotations to train these models. In this study, we develop a novel neural framework to extract abundant knowledge hidden in raw texts to empower the sequence labeling task. Besides word-level knowledge contained in pre-trained word embeddings, character-aware neural l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.04109","kind":"arxiv","version":4},"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":"1709.04109","created_at":"2026-05-18T00:29:45.568782+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.04109v4","created_at":"2026-05-18T00:29:45.568782+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.04109","created_at":"2026-05-18T00:29:45.568782+00:00"},{"alias_kind":"pith_short_12","alias_value":"OYM5P4IO327E","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_16","alias_value":"OYM5P4IO327EQPYG","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_8","alias_value":"OYM5P4IO","created_at":"2026-05-18T12:31:34.259226+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1906.11384","citing_title":"Eliciting Knowledge from Experts:Automatic Transcript Parsing for Cognitive Task Analysis","ref_index":11,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OYM5P4IO327EQPYGRIJX62EJIN","json":"https://pith.science/pith/OYM5P4IO327EQPYGRIJX62EJIN.json","graph_json":"https://pith.science/api/pith-number/OYM5P4IO327EQPYGRIJX62EJIN/graph.json","events_json":"https://pith.science/api/pith-number/OYM5P4IO327EQPYGRIJX62EJIN/events.json","paper":"https://pith.science/paper/OYM5P4IO"},"agent_actions":{"view_html":"https://pith.science/pith/OYM5P4IO327EQPYGRIJX62EJIN","download_json":"https://pith.science/pith/OYM5P4IO327EQPYGRIJX62EJIN.json","view_paper":"https://pith.science/paper/OYM5P4IO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.04109&json=true","fetch_graph":"https://pith.science/api/pith-number/OYM5P4IO327EQPYGRIJX62EJIN/graph.json","fetch_events":"https://pith.science/api/pith-number/OYM5P4IO327EQPYGRIJX62EJIN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OYM5P4IO327EQPYGRIJX62EJIN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OYM5P4IO327EQPYGRIJX62EJIN/action/storage_attestation","attest_author":"https://pith.science/pith/OYM5P4IO327EQPYGRIJX62EJIN/action/author_attestation","sign_citation":"https://pith.science/pith/OYM5P4IO327EQPYGRIJX62EJIN/action/citation_signature","submit_replication":"https://pith.science/pith/OYM5P4IO327EQPYGRIJX62EJIN/action/replication_record"}},"created_at":"2026-05-18T00:29:45.568782+00:00","updated_at":"2026-05-18T00:29:45.568782+00:00"}