{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:DP2UH2MBNRGH54E2JQRRYWJD4Z","short_pith_number":"pith:DP2UH2MB","schema_version":"1.0","canonical_sha256":"1bf543e9816c4c7ef09a4c231c5923e647276cd4e1198ddbcbf8687b32d7d4aa","source":{"kind":"arxiv","id":"1801.00625","version":1},"attestation_state":"computed","paper":{"title":"An Attentive Sequence Model for Adverse Drug Event Extraction from Biomedical Text","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Selvakumar Murugan, Suriyadeepan Ramamoorthy","submitted_at":"2018-01-02T12:19:08Z","abstract_excerpt":"Adverse reaction caused by drugs is a potentially dangerous problem which may lead to mortality and morbidity in patients. Adverse Drug Event (ADE) extraction is a significant problem in biomedical research. We model ADE extraction as a Question-Answering problem and take inspiration from Machine Reading Comprehension (MRC) literature, to design our model. Our objective in designing such a model, is to exploit the local linguistic context in clinical text and enable intra-sequence interaction, in order to jointly learn to classify drug and disease entities, and to extract adverse reactions cau"},"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":"1801.00625","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-01-02T12:19:08Z","cross_cats_sorted":[],"title_canon_sha256":"e5a19507757e103021306c30615318f6da339fe4a15524be6b52401102f20f5d","abstract_canon_sha256":"98fe0a50e8ead4c097588b87df273d24cf81ba58a2a1b62c958b75a129074cbb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:26:52.850615Z","signature_b64":"w7xVRbKlYV+875JDHKD8mGr7ekj+3O9P5guWa4edsLqY2tbnGPLz3do4g36gQjwA3gmkvTfCBT4XMSawEXkUCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1bf543e9816c4c7ef09a4c231c5923e647276cd4e1198ddbcbf8687b32d7d4aa","last_reissued_at":"2026-05-18T00:26:52.850085Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:26:52.850085Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Attentive Sequence Model for Adverse Drug Event Extraction from Biomedical Text","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Selvakumar Murugan, Suriyadeepan Ramamoorthy","submitted_at":"2018-01-02T12:19:08Z","abstract_excerpt":"Adverse reaction caused by drugs is a potentially dangerous problem which may lead to mortality and morbidity in patients. Adverse Drug Event (ADE) extraction is a significant problem in biomedical research. We model ADE extraction as a Question-Answering problem and take inspiration from Machine Reading Comprehension (MRC) literature, to design our model. Our objective in designing such a model, is to exploit the local linguistic context in clinical text and enable intra-sequence interaction, in order to jointly learn to classify drug and disease entities, and to extract adverse reactions cau"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.00625","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":"1801.00625","created_at":"2026-05-18T00:26:52.850163+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.00625v1","created_at":"2026-05-18T00:26:52.850163+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.00625","created_at":"2026-05-18T00:26:52.850163+00:00"},{"alias_kind":"pith_short_12","alias_value":"DP2UH2MBNRGH","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_16","alias_value":"DP2UH2MBNRGH54E2","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_8","alias_value":"DP2UH2MB","created_at":"2026-05-18T12:32:19.392346+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/DP2UH2MBNRGH54E2JQRRYWJD4Z","json":"https://pith.science/pith/DP2UH2MBNRGH54E2JQRRYWJD4Z.json","graph_json":"https://pith.science/api/pith-number/DP2UH2MBNRGH54E2JQRRYWJD4Z/graph.json","events_json":"https://pith.science/api/pith-number/DP2UH2MBNRGH54E2JQRRYWJD4Z/events.json","paper":"https://pith.science/paper/DP2UH2MB"},"agent_actions":{"view_html":"https://pith.science/pith/DP2UH2MBNRGH54E2JQRRYWJD4Z","download_json":"https://pith.science/pith/DP2UH2MBNRGH54E2JQRRYWJD4Z.json","view_paper":"https://pith.science/paper/DP2UH2MB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.00625&json=true","fetch_graph":"https://pith.science/api/pith-number/DP2UH2MBNRGH54E2JQRRYWJD4Z/graph.json","fetch_events":"https://pith.science/api/pith-number/DP2UH2MBNRGH54E2JQRRYWJD4Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DP2UH2MBNRGH54E2JQRRYWJD4Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DP2UH2MBNRGH54E2JQRRYWJD4Z/action/storage_attestation","attest_author":"https://pith.science/pith/DP2UH2MBNRGH54E2JQRRYWJD4Z/action/author_attestation","sign_citation":"https://pith.science/pith/DP2UH2MBNRGH54E2JQRRYWJD4Z/action/citation_signature","submit_replication":"https://pith.science/pith/DP2UH2MBNRGH54E2JQRRYWJD4Z/action/replication_record"}},"created_at":"2026-05-18T00:26:52.850163+00:00","updated_at":"2026-05-18T00:26:52.850163+00:00"}