{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:V2CXSDZP5SEBGOZ6Q62XPRPQHB","short_pith_number":"pith:V2CXSDZP","schema_version":"1.0","canonical_sha256":"ae85790f2fec88133b3e87b577c5f0385b22c29edae0c8679f4eda83ae6b5819","source":{"kind":"arxiv","id":"2011.00105","version":1},"attestation_state":"computed","paper":{"title":"Learning Structured Representations of Entity Names using Active Learning and Weak Supervision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Kun Qian, Lucian Popa, Poornima Chozhiyath Raman, Yunyao Li","submitted_at":"2020-10-30T21:01:22Z","abstract_excerpt":"Structured representations of entity names are useful for many entity-related tasks such as entity normalization and variant generation. Learning the implicit structured representations of entity names without context and external knowledge is particularly challenging. In this paper, we present a novel learning framework that combines active learning and weak supervision to solve this problem. Our experimental evaluation show that this framework enables the learning of high-quality models from merely a dozen or so labeled examples."},"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":"2011.00105","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-30T21:01:22Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f7295493a68bf6c18137aea481a1057e5a6947eeb6b4e5056542a540cd9fb29e","abstract_canon_sha256":"142c7a8b9eb1ba8ba245ca31ed19acec0d3be24daf04ae8b75c23a00fa4a44bd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:47:57.156468Z","signature_b64":"BplIcvZp4CtHjoiq7z1+R+n8NmrIVrS6f4Nj92cFPFdRhe1ffylBCpIn2INp2XOhekm+v09plIsDvRksPNmzAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae85790f2fec88133b3e87b577c5f0385b22c29edae0c8679f4eda83ae6b5819","last_reissued_at":"2026-07-05T01:47:57.156104Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:47:57.156104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Structured Representations of Entity Names using Active Learning and Weak Supervision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Kun Qian, Lucian Popa, Poornima Chozhiyath Raman, Yunyao Li","submitted_at":"2020-10-30T21:01:22Z","abstract_excerpt":"Structured representations of entity names are useful for many entity-related tasks such as entity normalization and variant generation. Learning the implicit structured representations of entity names without context and external knowledge is particularly challenging. In this paper, we present a novel learning framework that combines active learning and weak supervision to solve this problem. Our experimental evaluation show that this framework enables the learning of high-quality models from merely a dozen or so labeled examples."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.00105","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2011.00105/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2011.00105","created_at":"2026-07-05T01:47:57.156163+00:00"},{"alias_kind":"arxiv_version","alias_value":"2011.00105v1","created_at":"2026-07-05T01:47:57.156163+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.00105","created_at":"2026-07-05T01:47:57.156163+00:00"},{"alias_kind":"pith_short_12","alias_value":"V2CXSDZP5SEB","created_at":"2026-07-05T01:47:57.156163+00:00"},{"alias_kind":"pith_short_16","alias_value":"V2CXSDZP5SEBGOZ6","created_at":"2026-07-05T01:47:57.156163+00:00"},{"alias_kind":"pith_short_8","alias_value":"V2CXSDZP","created_at":"2026-07-05T01:47:57.156163+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.03292","citing_title":"ALPET: Active Few-shot Learning for Citation Worthiness Detection in Low-Resource Wikipedia Languages","ref_index":58,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/V2CXSDZP5SEBGOZ6Q62XPRPQHB","json":"https://pith.science/pith/V2CXSDZP5SEBGOZ6Q62XPRPQHB.json","graph_json":"https://pith.science/api/pith-number/V2CXSDZP5SEBGOZ6Q62XPRPQHB/graph.json","events_json":"https://pith.science/api/pith-number/V2CXSDZP5SEBGOZ6Q62XPRPQHB/events.json","paper":"https://pith.science/paper/V2CXSDZP"},"agent_actions":{"view_html":"https://pith.science/pith/V2CXSDZP5SEBGOZ6Q62XPRPQHB","download_json":"https://pith.science/pith/V2CXSDZP5SEBGOZ6Q62XPRPQHB.json","view_paper":"https://pith.science/paper/V2CXSDZP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2011.00105&json=true","fetch_graph":"https://pith.science/api/pith-number/V2CXSDZP5SEBGOZ6Q62XPRPQHB/graph.json","fetch_events":"https://pith.science/api/pith-number/V2CXSDZP5SEBGOZ6Q62XPRPQHB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V2CXSDZP5SEBGOZ6Q62XPRPQHB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V2CXSDZP5SEBGOZ6Q62XPRPQHB/action/storage_attestation","attest_author":"https://pith.science/pith/V2CXSDZP5SEBGOZ6Q62XPRPQHB/action/author_attestation","sign_citation":"https://pith.science/pith/V2CXSDZP5SEBGOZ6Q62XPRPQHB/action/citation_signature","submit_replication":"https://pith.science/pith/V2CXSDZP5SEBGOZ6Q62XPRPQHB/action/replication_record"}},"created_at":"2026-07-05T01:47:57.156163+00:00","updated_at":"2026-07-05T01:47:57.156163+00:00"}