{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:ZFUCKAUZFWEIHKXFHAFW2X7LS2","short_pith_number":"pith:ZFUCKAUZ","schema_version":"1.0","canonical_sha256":"c9682502992d8883aae5380b6d5feb96adbe90bcd25ed66137dcf76a52bf598d","source":{"kind":"arxiv","id":"1707.08852","version":1},"attestation_state":"computed","paper":{"title":"Detecting and Explaining Causes From Text For a Time Series Event","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Ang Lu, Dongyeop Kang, Eduard Hovy, Varun Gangal, Zheng Chen","submitted_at":"2017-07-27T13:14:57Z","abstract_excerpt":"Explaining underlying causes or effects about events is a challenging but valuable task. We define a novel problem of generating explanations of a time series event by (1) searching cause and effect relationships of the time series with textual data and (2) constructing a connecting chain between them to generate an explanation. To detect causal features from text, we propose a novel method based on the Granger causality of time series between features extracted from text such as N-grams, topics, sentiments, and their composition. The generation of the sequence of causal entities requires a co"},"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":"1707.08852","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-07-27T13:14:57Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"69acaac724dc5e35cf8d19b1b384d2fdffce38ef4354ca06b87ef4a83f0c74ab","abstract_canon_sha256":"e0ca66e528a27c78cf0dbad782d0a4fc5c118c434be1ae798081e4c418ca7c87"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:17:33.395710Z","signature_b64":"/WXt0Mmli5owfirrYA+V60c//1OTfNNFGSYiwOEdIF7MmgvA5+XxiKjayNywr+GjEiLBi6GF3pVWsbjCIp2zCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c9682502992d8883aae5380b6d5feb96adbe90bcd25ed66137dcf76a52bf598d","last_reissued_at":"2026-05-18T00:17:33.395248Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:17:33.395248Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Detecting and Explaining Causes From Text For a Time Series Event","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Ang Lu, Dongyeop Kang, Eduard Hovy, Varun Gangal, Zheng Chen","submitted_at":"2017-07-27T13:14:57Z","abstract_excerpt":"Explaining underlying causes or effects about events is a challenging but valuable task. We define a novel problem of generating explanations of a time series event by (1) searching cause and effect relationships of the time series with textual data and (2) constructing a connecting chain between them to generate an explanation. To detect causal features from text, we propose a novel method based on the Granger causality of time series between features extracted from text such as N-grams, topics, sentiments, and their composition. The generation of the sequence of causal entities requires a co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.08852","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":"1707.08852","created_at":"2026-05-18T00:17:33.395321+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.08852v1","created_at":"2026-05-18T00:17:33.395321+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.08852","created_at":"2026-05-18T00:17:33.395321+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZFUCKAUZFWEI","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZFUCKAUZFWEIHKXF","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZFUCKAUZ","created_at":"2026-05-18T12:31:59.375834+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"1904.09728","citing_title":"SocialIQA: Commonsense Reasoning about Social Interactions","ref_index":141,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZFUCKAUZFWEIHKXFHAFW2X7LS2","json":"https://pith.science/pith/ZFUCKAUZFWEIHKXFHAFW2X7LS2.json","graph_json":"https://pith.science/api/pith-number/ZFUCKAUZFWEIHKXFHAFW2X7LS2/graph.json","events_json":"https://pith.science/api/pith-number/ZFUCKAUZFWEIHKXFHAFW2X7LS2/events.json","paper":"https://pith.science/paper/ZFUCKAUZ"},"agent_actions":{"view_html":"https://pith.science/pith/ZFUCKAUZFWEIHKXFHAFW2X7LS2","download_json":"https://pith.science/pith/ZFUCKAUZFWEIHKXFHAFW2X7LS2.json","view_paper":"https://pith.science/paper/ZFUCKAUZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.08852&json=true","fetch_graph":"https://pith.science/api/pith-number/ZFUCKAUZFWEIHKXFHAFW2X7LS2/graph.json","fetch_events":"https://pith.science/api/pith-number/ZFUCKAUZFWEIHKXFHAFW2X7LS2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZFUCKAUZFWEIHKXFHAFW2X7LS2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZFUCKAUZFWEIHKXFHAFW2X7LS2/action/storage_attestation","attest_author":"https://pith.science/pith/ZFUCKAUZFWEIHKXFHAFW2X7LS2/action/author_attestation","sign_citation":"https://pith.science/pith/ZFUCKAUZFWEIHKXFHAFW2X7LS2/action/citation_signature","submit_replication":"https://pith.science/pith/ZFUCKAUZFWEIHKXFHAFW2X7LS2/action/replication_record"}},"created_at":"2026-05-18T00:17:33.395321+00:00","updated_at":"2026-05-18T00:17:33.395321+00:00"}