{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:JDG3MEVWHBEYCFLLA4FBX27H3U","short_pith_number":"pith:JDG3MEVW","schema_version":"1.0","canonical_sha256":"48cdb612b6384981156b070a1bebe7dd25195ad2d6b0bd2ece0c3978955ffade","source":{"kind":"arxiv","id":"1906.01378","version":2},"attestation_state":"computed","paper":{"title":"Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jinlan Fu, Minlong Peng, Qi Zhang, Xiaoyu Xing, Xuanjing Huang","submitted_at":"2019-06-04T12:39:10Z","abstract_excerpt":"In this work, we explore the way to perform named entity recognition (NER) using only unlabeled data and named entity dictionaries. To this end, we formulate the task as a positive-unlabeled (PU) learning problem and accordingly propose a novel PU learning algorithm to perform the task. We prove that the proposed algorithm can unbiasedly and consistently estimate the task loss as if there is fully labeled data. A key feature of the proposed method is that it does not require the dictionaries to label every entity within a sentence, and it even does not require the dictionaries to label all of "},"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":"1906.01378","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-04T12:39:10Z","cross_cats_sorted":[],"title_canon_sha256":"104ba7801de08a8207c7abce16f62f2642ee9c8746d916c6c39da47ff22534e8","abstract_canon_sha256":"5072b35617b25af1c9b93c15dd86681c10efed6ab1996c909c112483ffa7130b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:42.137328Z","signature_b64":"KpLKTTscKoVDsI7QGoB+1/g+KYfV1eQW1Aygny+wGHev91LIMxkrY7R7d2Pa2lnH6LKLxohbG/ZAHMsCcpddDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"48cdb612b6384981156b070a1bebe7dd25195ad2d6b0bd2ece0c3978955ffade","last_reissued_at":"2026-05-17T23:43:42.136717Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:42.136717Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jinlan Fu, Minlong Peng, Qi Zhang, Xiaoyu Xing, Xuanjing Huang","submitted_at":"2019-06-04T12:39:10Z","abstract_excerpt":"In this work, we explore the way to perform named entity recognition (NER) using only unlabeled data and named entity dictionaries. To this end, we formulate the task as a positive-unlabeled (PU) learning problem and accordingly propose a novel PU learning algorithm to perform the task. We prove that the proposed algorithm can unbiasedly and consistently estimate the task loss as if there is fully labeled data. A key feature of the proposed method is that it does not require the dictionaries to label every entity within a sentence, and it even does not require the dictionaries to label all of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.01378","kind":"arxiv","version":2},"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":"1906.01378","created_at":"2026-05-17T23:43:42.136797+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.01378v2","created_at":"2026-05-17T23:43:42.136797+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.01378","created_at":"2026-05-17T23:43:42.136797+00:00"},{"alias_kind":"pith_short_12","alias_value":"JDG3MEVWHBEY","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_16","alias_value":"JDG3MEVWHBEYCFLL","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_8","alias_value":"JDG3MEVW","created_at":"2026-05-18T12:33:18.533446+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.12454","citing_title":"Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JDG3MEVWHBEYCFLLA4FBX27H3U","json":"https://pith.science/pith/JDG3MEVWHBEYCFLLA4FBX27H3U.json","graph_json":"https://pith.science/api/pith-number/JDG3MEVWHBEYCFLLA4FBX27H3U/graph.json","events_json":"https://pith.science/api/pith-number/JDG3MEVWHBEYCFLLA4FBX27H3U/events.json","paper":"https://pith.science/paper/JDG3MEVW"},"agent_actions":{"view_html":"https://pith.science/pith/JDG3MEVWHBEYCFLLA4FBX27H3U","download_json":"https://pith.science/pith/JDG3MEVWHBEYCFLLA4FBX27H3U.json","view_paper":"https://pith.science/paper/JDG3MEVW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.01378&json=true","fetch_graph":"https://pith.science/api/pith-number/JDG3MEVWHBEYCFLLA4FBX27H3U/graph.json","fetch_events":"https://pith.science/api/pith-number/JDG3MEVWHBEYCFLLA4FBX27H3U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JDG3MEVWHBEYCFLLA4FBX27H3U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JDG3MEVWHBEYCFLLA4FBX27H3U/action/storage_attestation","attest_author":"https://pith.science/pith/JDG3MEVWHBEYCFLLA4FBX27H3U/action/author_attestation","sign_citation":"https://pith.science/pith/JDG3MEVWHBEYCFLLA4FBX27H3U/action/citation_signature","submit_replication":"https://pith.science/pith/JDG3MEVWHBEYCFLLA4FBX27H3U/action/replication_record"}},"created_at":"2026-05-17T23:43:42.136797+00:00","updated_at":"2026-05-17T23:43:42.136797+00:00"}