{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:BHQU47PV7LEVZYY5MIA2DO3FWP","short_pith_number":"pith:BHQU47PV","schema_version":"1.0","canonical_sha256":"09e14e7df5fac95ce31d6201a1bb65b3e2b4645ef4d57f34e7a9b871555cdfb5","source":{"kind":"arxiv","id":"2105.01819","version":1},"attestation_state":"computed","paper":{"title":"ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alexander Zamanian, Benjamin Rozonoyer, Bonan Min, Haoling Qiu, Jessica MacBride","submitted_at":"2021-05-05T01:18:46Z","abstract_excerpt":"Timely responses from policy makers to mitigate the impact of the COVID-19 pandemic rely on a comprehensive grasp of events, their causes, and their impacts. These events are reported at such a speed and scale as to be overwhelming. In this paper, we present ExcavatorCovid, a machine reading system that ingests open-source text documents (e.g., news and scientific publications), extracts COVID19 related events and relations between them, and builds a Temporal and Causal Analysis Graph (TCAG). Excavator will help government agencies alleviate the information overload, understand likely downstre"},"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":"2105.01819","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-05-05T01:18:46Z","cross_cats_sorted":[],"title_canon_sha256":"9e05bf2cea19c0ceed54ec61591aa1aaf105c0f9389543a1382c134f655fea71","abstract_canon_sha256":"d2e71b29fedf75571ee82b7f789a1ba76ccef3cd1b36232445abd30fd6f83977"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:37:48.706940Z","signature_b64":"+WpLVTsiRcmGyuRITu2J3nHMlv12zkJHJ4w+FJTZY5NZRcBEQGRc5wmp4rDBW4XoiNwOHYuRtIu+GpkA4myZBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"09e14e7df5fac95ce31d6201a1bb65b3e2b4645ef4d57f34e7a9b871555cdfb5","last_reissued_at":"2026-07-05T02:37:48.706529Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:37:48.706529Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alexander Zamanian, Benjamin Rozonoyer, Bonan Min, Haoling Qiu, Jessica MacBride","submitted_at":"2021-05-05T01:18:46Z","abstract_excerpt":"Timely responses from policy makers to mitigate the impact of the COVID-19 pandemic rely on a comprehensive grasp of events, their causes, and their impacts. These events are reported at such a speed and scale as to be overwhelming. In this paper, we present ExcavatorCovid, a machine reading system that ingests open-source text documents (e.g., news and scientific publications), extracts COVID19 related events and relations between them, and builds a Temporal and Causal Analysis Graph (TCAG). Excavator will help government agencies alleviate the information overload, understand likely downstre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.01819","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/2105.01819/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":"2105.01819","created_at":"2026-07-05T02:37:48.706591+00:00"},{"alias_kind":"arxiv_version","alias_value":"2105.01819v1","created_at":"2026-07-05T02:37:48.706591+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.01819","created_at":"2026-07-05T02:37:48.706591+00:00"},{"alias_kind":"pith_short_12","alias_value":"BHQU47PV7LEV","created_at":"2026-07-05T02:37:48.706591+00:00"},{"alias_kind":"pith_short_16","alias_value":"BHQU47PV7LEVZYY5","created_at":"2026-07-05T02:37:48.706591+00:00"},{"alias_kind":"pith_short_8","alias_value":"BHQU47PV","created_at":"2026-07-05T02:37:48.706591+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/BHQU47PV7LEVZYY5MIA2DO3FWP","json":"https://pith.science/pith/BHQU47PV7LEVZYY5MIA2DO3FWP.json","graph_json":"https://pith.science/api/pith-number/BHQU47PV7LEVZYY5MIA2DO3FWP/graph.json","events_json":"https://pith.science/api/pith-number/BHQU47PV7LEVZYY5MIA2DO3FWP/events.json","paper":"https://pith.science/paper/BHQU47PV"},"agent_actions":{"view_html":"https://pith.science/pith/BHQU47PV7LEVZYY5MIA2DO3FWP","download_json":"https://pith.science/pith/BHQU47PV7LEVZYY5MIA2DO3FWP.json","view_paper":"https://pith.science/paper/BHQU47PV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2105.01819&json=true","fetch_graph":"https://pith.science/api/pith-number/BHQU47PV7LEVZYY5MIA2DO3FWP/graph.json","fetch_events":"https://pith.science/api/pith-number/BHQU47PV7LEVZYY5MIA2DO3FWP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BHQU47PV7LEVZYY5MIA2DO3FWP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BHQU47PV7LEVZYY5MIA2DO3FWP/action/storage_attestation","attest_author":"https://pith.science/pith/BHQU47PV7LEVZYY5MIA2DO3FWP/action/author_attestation","sign_citation":"https://pith.science/pith/BHQU47PV7LEVZYY5MIA2DO3FWP/action/citation_signature","submit_replication":"https://pith.science/pith/BHQU47PV7LEVZYY5MIA2DO3FWP/action/replication_record"}},"created_at":"2026-07-05T02:37:48.706591+00:00","updated_at":"2026-07-05T02:37:48.706591+00:00"}