{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:E6IPE6Z22Y3TE4ONKN6PCS4DDF","short_pith_number":"pith:E6IPE6Z2","schema_version":"1.0","canonical_sha256":"2790f27b3ad6373271cd537cf14b831962909a8a3b4539f65e4db15b3048c21a","source":{"kind":"arxiv","id":"1808.09397","version":1},"attestation_state":"computed","paper":{"title":"MedSTS: A Resource for Clinical Semantic Textual Similarity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Feichen Shen, Hongfang Liu, Liwei Wang, Majid Rastegar-Mojarad, Naveed Afzal, Sunyang Fu, Yanshan Wang","submitted_at":"2018-08-28T16:43:19Z","abstract_excerpt":"The wide adoption of electronic health records (EHRs) has enabled a wide range of applications leveraging EHR data. However, the meaningful use of EHR data largely depends on our ability to efficiently extract and consolidate information embedded in clinical text where natural language processing (NLP) techniques are essential. Semantic textual similarity (STS) that measures the semantic similarity between text snippets plays a significant role in many NLP applications. In the general NLP domain, STS shared tasks have made available a huge collection of text snippet pairs with manual annotatio"},"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":"1808.09397","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-08-28T16:43:19Z","cross_cats_sorted":[],"title_canon_sha256":"a165b66298ea3e16fa3d82b0b6af0efc7cef96c959de4b28cf899caa6edad7fd","abstract_canon_sha256":"0e4657f6ce6bdd8114eb67adeed7726dd24fc5c978cd6ee0e449cf38ec420592"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:01.506581Z","signature_b64":"pYIDzic+HWZKjn03odd5kwNOJTdGBmVC6kobv/6pNW1h4Obhjdwt8PHvoby5LR0IIMkILlh7nRY1p806KwIBDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2790f27b3ad6373271cd537cf14b831962909a8a3b4539f65e4db15b3048c21a","last_reissued_at":"2026-05-18T00:07:01.506075Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:01.506075Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MedSTS: A Resource for Clinical Semantic Textual Similarity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Feichen Shen, Hongfang Liu, Liwei Wang, Majid Rastegar-Mojarad, Naveed Afzal, Sunyang Fu, Yanshan Wang","submitted_at":"2018-08-28T16:43:19Z","abstract_excerpt":"The wide adoption of electronic health records (EHRs) has enabled a wide range of applications leveraging EHR data. However, the meaningful use of EHR data largely depends on our ability to efficiently extract and consolidate information embedded in clinical text where natural language processing (NLP) techniques are essential. Semantic textual similarity (STS) that measures the semantic similarity between text snippets plays a significant role in many NLP applications. In the general NLP domain, STS shared tasks have made available a huge collection of text snippet pairs with manual annotatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.09397","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":"1808.09397","created_at":"2026-05-18T00:07:01.506164+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.09397v1","created_at":"2026-05-18T00:07:01.506164+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.09397","created_at":"2026-05-18T00:07:01.506164+00:00"},{"alias_kind":"pith_short_12","alias_value":"E6IPE6Z22Y3T","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_16","alias_value":"E6IPE6Z22Y3TE4ON","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_8","alias_value":"E6IPE6Z2","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/E6IPE6Z22Y3TE4ONKN6PCS4DDF","json":"https://pith.science/pith/E6IPE6Z22Y3TE4ONKN6PCS4DDF.json","graph_json":"https://pith.science/api/pith-number/E6IPE6Z22Y3TE4ONKN6PCS4DDF/graph.json","events_json":"https://pith.science/api/pith-number/E6IPE6Z22Y3TE4ONKN6PCS4DDF/events.json","paper":"https://pith.science/paper/E6IPE6Z2"},"agent_actions":{"view_html":"https://pith.science/pith/E6IPE6Z22Y3TE4ONKN6PCS4DDF","download_json":"https://pith.science/pith/E6IPE6Z22Y3TE4ONKN6PCS4DDF.json","view_paper":"https://pith.science/paper/E6IPE6Z2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.09397&json=true","fetch_graph":"https://pith.science/api/pith-number/E6IPE6Z22Y3TE4ONKN6PCS4DDF/graph.json","fetch_events":"https://pith.science/api/pith-number/E6IPE6Z22Y3TE4ONKN6PCS4DDF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E6IPE6Z22Y3TE4ONKN6PCS4DDF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E6IPE6Z22Y3TE4ONKN6PCS4DDF/action/storage_attestation","attest_author":"https://pith.science/pith/E6IPE6Z22Y3TE4ONKN6PCS4DDF/action/author_attestation","sign_citation":"https://pith.science/pith/E6IPE6Z22Y3TE4ONKN6PCS4DDF/action/citation_signature","submit_replication":"https://pith.science/pith/E6IPE6Z22Y3TE4ONKN6PCS4DDF/action/replication_record"}},"created_at":"2026-05-18T00:07:01.506164+00:00","updated_at":"2026-05-18T00:07:01.506164+00:00"}