{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:PCVSTAHEZVQ52BG6EGCYIEGBMJ","short_pith_number":"pith:PCVSTAHE","schema_version":"1.0","canonical_sha256":"78ab2980e4cd61dd04de21858410c162698b722b7cf98a43337ed180b6424087","source":{"kind":"arxiv","id":"1809.02700","version":1},"attestation_state":"computed","paper":{"title":"Textual Analogy Parsing: What's Shared and What's Compared among Analogous Facts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Arun Tejasvi Chaganty, Christopher D. Manning, Dan Jurafsky, Matthew Lamm, Percy Liang","submitted_at":"2018-09-07T22:22:26Z","abstract_excerpt":"To understand a sentence like \"whereas only 10% of White Americans live at or below the poverty line, 28% of African Americans do\" it is important not only to identify individual facts, e.g., poverty rates of distinct demographic groups, but also the higher-order relations between them, e.g., the disparity between them. In this paper, we propose the task of Textual Analogy Parsing (TAP) to model this higher-order meaning. The output of TAP is a frame-style meaning representation which explicitly specifies what is shared (e.g., poverty rates) and what is compared (e.g., White Americans vs. Afri"},"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":"1809.02700","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-09-07T22:22:26Z","cross_cats_sorted":[],"title_canon_sha256":"18a536ae81f8599e2e7e45e6a4da95b5ed50b7c0fa9641914487a85dcdce7206","abstract_canon_sha256":"3c192ee4e5ca801e6da6398c477053c3f1643d559482685b6c9e9ab8d66e7924"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:15.574524Z","signature_b64":"dEmk52Tnnrb7WhlUdikk/YAC4nDWA4XlE8/xPbl5vlijN7WSJpzWOr6QuhEc6/GChaGFdytvvkpjD+qcrm2PBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78ab2980e4cd61dd04de21858410c162698b722b7cf98a43337ed180b6424087","last_reissued_at":"2026-05-18T00:06:15.574027Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:15.574027Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Textual Analogy Parsing: What's Shared and What's Compared among Analogous Facts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Arun Tejasvi Chaganty, Christopher D. Manning, Dan Jurafsky, Matthew Lamm, Percy Liang","submitted_at":"2018-09-07T22:22:26Z","abstract_excerpt":"To understand a sentence like \"whereas only 10% of White Americans live at or below the poverty line, 28% of African Americans do\" it is important not only to identify individual facts, e.g., poverty rates of distinct demographic groups, but also the higher-order relations between them, e.g., the disparity between them. In this paper, we propose the task of Textual Analogy Parsing (TAP) to model this higher-order meaning. The output of TAP is a frame-style meaning representation which explicitly specifies what is shared (e.g., poverty rates) and what is compared (e.g., White Americans vs. Afri"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.02700","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":"1809.02700","created_at":"2026-05-18T00:06:15.574100+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.02700v1","created_at":"2026-05-18T00:06:15.574100+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.02700","created_at":"2026-05-18T00:06:15.574100+00:00"},{"alias_kind":"pith_short_12","alias_value":"PCVSTAHEZVQ5","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_16","alias_value":"PCVSTAHEZVQ52BG6","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_8","alias_value":"PCVSTAHE","created_at":"2026-05-18T12:32:43.782077+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.09091","citing_title":"Text-to-Viz: Automatic Generation of Infographics from Proportion-Related Natural Language Statements","ref_index":34,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PCVSTAHEZVQ52BG6EGCYIEGBMJ","json":"https://pith.science/pith/PCVSTAHEZVQ52BG6EGCYIEGBMJ.json","graph_json":"https://pith.science/api/pith-number/PCVSTAHEZVQ52BG6EGCYIEGBMJ/graph.json","events_json":"https://pith.science/api/pith-number/PCVSTAHEZVQ52BG6EGCYIEGBMJ/events.json","paper":"https://pith.science/paper/PCVSTAHE"},"agent_actions":{"view_html":"https://pith.science/pith/PCVSTAHEZVQ52BG6EGCYIEGBMJ","download_json":"https://pith.science/pith/PCVSTAHEZVQ52BG6EGCYIEGBMJ.json","view_paper":"https://pith.science/paper/PCVSTAHE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.02700&json=true","fetch_graph":"https://pith.science/api/pith-number/PCVSTAHEZVQ52BG6EGCYIEGBMJ/graph.json","fetch_events":"https://pith.science/api/pith-number/PCVSTAHEZVQ52BG6EGCYIEGBMJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PCVSTAHEZVQ52BG6EGCYIEGBMJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PCVSTAHEZVQ52BG6EGCYIEGBMJ/action/storage_attestation","attest_author":"https://pith.science/pith/PCVSTAHEZVQ52BG6EGCYIEGBMJ/action/author_attestation","sign_citation":"https://pith.science/pith/PCVSTAHEZVQ52BG6EGCYIEGBMJ/action/citation_signature","submit_replication":"https://pith.science/pith/PCVSTAHEZVQ52BG6EGCYIEGBMJ/action/replication_record"}},"created_at":"2026-05-18T00:06:15.574100+00:00","updated_at":"2026-05-18T00:06:15.574100+00:00"}