{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:R5KJB7UFCSFTRGSD3L2FW2HGI7","short_pith_number":"pith:R5KJB7UF","schema_version":"1.0","canonical_sha256":"8f5490fe85148b389a43daf45b68e647eb03c865db0c3d41a6bf802f8d680657","source":{"kind":"arxiv","id":"1908.05691","version":1},"attestation_state":"computed","paper":{"title":"Improving Multi-Word Entity Recognition for Biomedical Texts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hamada A. Nayel, Hiroyuki Shindo, H. L. Shashirekha, Yuji Matsumoto","submitted_at":"2019-08-15T18:04:39Z","abstract_excerpt":"Biomedical Named Entity Recognition (BioNER) is a crucial step for analyzing Biomedical texts, which aims at extracting biomedical named entities from a given text. Different supervised machine learning algorithms have been applied for BioNER by various researchers. The main requirement of these approaches is an annotated dataset used for learning the parameters of machine learning algorithms. Segment Representation (SR) models comprise of different tag sets used for representing the annotated data, such as IOB2, IOE2 and IOBES. In this paper, we propose an extension of IOBES model to improve "},"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":"1908.05691","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-15T18:04:39Z","cross_cats_sorted":[],"title_canon_sha256":"2fd40be5df48bfa13f300fd26ec2717ea1405e59c16bea8476ae169f22ff0bf0","abstract_canon_sha256":"4a1d938b5b30fc97a63bdb72ddcebf021718742145ef8627c6bd108b7435c41d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:57:52.434293Z","signature_b64":"4L2lw46osXmnSH6ypEGfrc4N81yA8JgURWt/WtCDt0RmjrK/w15iZMfNquh2PrbTJAhDcloVud+mw+ZLmXKbCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f5490fe85148b389a43daf45b68e647eb03c865db0c3d41a6bf802f8d680657","last_reissued_at":"2026-07-04T23:57:52.433909Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:57:52.433909Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Multi-Word Entity Recognition for Biomedical Texts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hamada A. Nayel, Hiroyuki Shindo, H. L. Shashirekha, Yuji Matsumoto","submitted_at":"2019-08-15T18:04:39Z","abstract_excerpt":"Biomedical Named Entity Recognition (BioNER) is a crucial step for analyzing Biomedical texts, which aims at extracting biomedical named entities from a given text. Different supervised machine learning algorithms have been applied for BioNER by various researchers. The main requirement of these approaches is an annotated dataset used for learning the parameters of machine learning algorithms. Segment Representation (SR) models comprise of different tag sets used for representing the annotated data, such as IOB2, IOE2 and IOBES. In this paper, we propose an extension of IOBES model to improve "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.05691","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/1908.05691/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":"1908.05691","created_at":"2026-07-04T23:57:52.433978+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.05691v1","created_at":"2026-07-04T23:57:52.433978+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.05691","created_at":"2026-07-04T23:57:52.433978+00:00"},{"alias_kind":"pith_short_12","alias_value":"R5KJB7UFCSFT","created_at":"2026-07-04T23:57:52.433978+00:00"},{"alias_kind":"pith_short_16","alias_value":"R5KJB7UFCSFTRGSD","created_at":"2026-07-04T23:57:52.433978+00:00"},{"alias_kind":"pith_short_8","alias_value":"R5KJB7UF","created_at":"2026-07-04T23:57:52.433978+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/R5KJB7UFCSFTRGSD3L2FW2HGI7","json":"https://pith.science/pith/R5KJB7UFCSFTRGSD3L2FW2HGI7.json","graph_json":"https://pith.science/api/pith-number/R5KJB7UFCSFTRGSD3L2FW2HGI7/graph.json","events_json":"https://pith.science/api/pith-number/R5KJB7UFCSFTRGSD3L2FW2HGI7/events.json","paper":"https://pith.science/paper/R5KJB7UF"},"agent_actions":{"view_html":"https://pith.science/pith/R5KJB7UFCSFTRGSD3L2FW2HGI7","download_json":"https://pith.science/pith/R5KJB7UFCSFTRGSD3L2FW2HGI7.json","view_paper":"https://pith.science/paper/R5KJB7UF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.05691&json=true","fetch_graph":"https://pith.science/api/pith-number/R5KJB7UFCSFTRGSD3L2FW2HGI7/graph.json","fetch_events":"https://pith.science/api/pith-number/R5KJB7UFCSFTRGSD3L2FW2HGI7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R5KJB7UFCSFTRGSD3L2FW2HGI7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R5KJB7UFCSFTRGSD3L2FW2HGI7/action/storage_attestation","attest_author":"https://pith.science/pith/R5KJB7UFCSFTRGSD3L2FW2HGI7/action/author_attestation","sign_citation":"https://pith.science/pith/R5KJB7UFCSFTRGSD3L2FW2HGI7/action/citation_signature","submit_replication":"https://pith.science/pith/R5KJB7UFCSFTRGSD3L2FW2HGI7/action/replication_record"}},"created_at":"2026-07-04T23:57:52.433978+00:00","updated_at":"2026-07-04T23:57:52.433978+00:00"}