{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:RMENDWSLTSXMBQQ6ST4RTVBUNN","short_pith_number":"pith:RMENDWSL","schema_version":"1.0","canonical_sha256":"8b08d1da4b9caec0c21e94f919d4346b47fd6f8186a1185eb269cf232be380f6","source":{"kind":"arxiv","id":"1107.4573","version":1},"attestation_state":"computed","paper":{"title":"Analogy perception applied to seven tests of word comprehension","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.AI","authors_text":"Peter D. Turney (National Research Council of Canada)","submitted_at":"2011-07-22T16:54:11Z","abstract_excerpt":"It has been argued that analogy is the core of cognition. In AI research, algorithms for analogy are often limited by the need for hand-coded high-level representations as input. An alternative approach is to use high-level perception, in which high-level representations are automatically generated from raw data. Analogy perception is the process of recognizing analogies using high-level perception. We present PairClass, an algorithm for analogy perception that recognizes lexical proportional analogies using representations that are automatically generated from a large corpus of raw textual da"},"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":"1107.4573","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2011-07-22T16:54:11Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"e0483e29ced135418aca56de66f645368571dd4e4840cd9a1bac705e4842b81f","abstract_canon_sha256":"89577e57959e7ec7c552cb35a76a2e9621345a8cd150aa5188d99ae3c9b96b28"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:17:03.748122Z","signature_b64":"aYarAuB31TMyLvM0YYVAKoTO1TxrsUqw1q88u0m3IAeeOQJghhUN9jno2BwnXhH8ARpv0fFzHmk0LyYfKdWrAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8b08d1da4b9caec0c21e94f919d4346b47fd6f8186a1185eb269cf232be380f6","last_reissued_at":"2026-05-18T04:17:03.747570Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:17:03.747570Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Analogy perception applied to seven tests of word comprehension","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.AI","authors_text":"Peter D. Turney (National Research Council of Canada)","submitted_at":"2011-07-22T16:54:11Z","abstract_excerpt":"It has been argued that analogy is the core of cognition. In AI research, algorithms for analogy are often limited by the need for hand-coded high-level representations as input. An alternative approach is to use high-level perception, in which high-level representations are automatically generated from raw data. Analogy perception is the process of recognizing analogies using high-level perception. We present PairClass, an algorithm for analogy perception that recognizes lexical proportional analogies using representations that are automatically generated from a large corpus of raw textual da"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1107.4573","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":"1107.4573","created_at":"2026-05-18T04:17:03.747631+00:00"},{"alias_kind":"arxiv_version","alias_value":"1107.4573v1","created_at":"2026-05-18T04:17:03.747631+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1107.4573","created_at":"2026-05-18T04:17:03.747631+00:00"},{"alias_kind":"pith_short_12","alias_value":"RMENDWSLTSXM","created_at":"2026-05-18T12:26:41.206345+00:00"},{"alias_kind":"pith_short_16","alias_value":"RMENDWSLTSXMBQQ6","created_at":"2026-05-18T12:26:41.206345+00:00"},{"alias_kind":"pith_short_8","alias_value":"RMENDWSL","created_at":"2026-05-18T12:26:41.206345+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/RMENDWSLTSXMBQQ6ST4RTVBUNN","json":"https://pith.science/pith/RMENDWSLTSXMBQQ6ST4RTVBUNN.json","graph_json":"https://pith.science/api/pith-number/RMENDWSLTSXMBQQ6ST4RTVBUNN/graph.json","events_json":"https://pith.science/api/pith-number/RMENDWSLTSXMBQQ6ST4RTVBUNN/events.json","paper":"https://pith.science/paper/RMENDWSL"},"agent_actions":{"view_html":"https://pith.science/pith/RMENDWSLTSXMBQQ6ST4RTVBUNN","download_json":"https://pith.science/pith/RMENDWSLTSXMBQQ6ST4RTVBUNN.json","view_paper":"https://pith.science/paper/RMENDWSL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1107.4573&json=true","fetch_graph":"https://pith.science/api/pith-number/RMENDWSLTSXMBQQ6ST4RTVBUNN/graph.json","fetch_events":"https://pith.science/api/pith-number/RMENDWSLTSXMBQQ6ST4RTVBUNN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RMENDWSLTSXMBQQ6ST4RTVBUNN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RMENDWSLTSXMBQQ6ST4RTVBUNN/action/storage_attestation","attest_author":"https://pith.science/pith/RMENDWSLTSXMBQQ6ST4RTVBUNN/action/author_attestation","sign_citation":"https://pith.science/pith/RMENDWSLTSXMBQQ6ST4RTVBUNN/action/citation_signature","submit_replication":"https://pith.science/pith/RMENDWSLTSXMBQQ6ST4RTVBUNN/action/replication_record"}},"created_at":"2026-05-18T04:17:03.747631+00:00","updated_at":"2026-05-18T04:17:03.747631+00:00"}