{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:W5DUGWF4MIIXW2OQRWZ2U3GJN4","short_pith_number":"pith:W5DUGWF4","schema_version":"1.0","canonical_sha256":"b7474358bc62117b69d08db3aa6cc96f062cb1188ec07b41702f029dfcd6510a","source":{"kind":"arxiv","id":"1802.01241","version":2},"attestation_state":"computed","paper":{"title":"Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Evelina Fedorenko, Francisco Pereira, Gabriel Grand, Idan Asher Blank","submitted_at":"2018-02-05T02:42:40Z","abstract_excerpt":"The words of a language reflect the structure of the human mind, allowing us to transmit thoughts between individuals. However, language can represent only a subset of our rich and detailed cognitive architecture. Here, we ask what kinds of common knowledge (semantic memory) are captured by word meanings (lexical semantics). We examine a prominent computational model that represents words as vectors in a multidimensional space, such that proximity between word-vectors approximates semantic relatedness. Because related words appear in similar contexts, such spaces - called \"word embeddings\" - c"},"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":"1802.01241","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-02-05T02:42:40Z","cross_cats_sorted":[],"title_canon_sha256":"eb5c0c6bd4cfe4bfbdc6eafd6218b08e1031e43dab963100cb87d7df0af67ee9","abstract_canon_sha256":"a36feee7c0f308d1212309d6f9a9cd465be3f725f748d74b182550a57378ef42"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:21:56.604727Z","signature_b64":"ibu9Ht8fl+zaswrMPLAfkoHb/fakLWEAO/qRU2xYOR9YNoKslDCmf5zzMxxnv37UNpyBomZidhpUCOfQswYRBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7474358bc62117b69d08db3aa6cc96f062cb1188ec07b41702f029dfcd6510a","last_reissued_at":"2026-05-18T00:21:56.604124Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:21:56.604124Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Evelina Fedorenko, Francisco Pereira, Gabriel Grand, Idan Asher Blank","submitted_at":"2018-02-05T02:42:40Z","abstract_excerpt":"The words of a language reflect the structure of the human mind, allowing us to transmit thoughts between individuals. However, language can represent only a subset of our rich and detailed cognitive architecture. Here, we ask what kinds of common knowledge (semantic memory) are captured by word meanings (lexical semantics). We examine a prominent computational model that represents words as vectors in a multidimensional space, such that proximity between word-vectors approximates semantic relatedness. Because related words appear in similar contexts, such spaces - called \"word embeddings\" - c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.01241","kind":"arxiv","version":2},"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":"1802.01241","created_at":"2026-05-18T00:21:56.604227+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.01241v2","created_at":"2026-05-18T00:21:56.604227+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.01241","created_at":"2026-05-18T00:21:56.604227+00:00"},{"alias_kind":"pith_short_12","alias_value":"W5DUGWF4MIIX","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_16","alias_value":"W5DUGWF4MIIXW2OQ","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_8","alias_value":"W5DUGWF4","created_at":"2026-05-18T12:32:59.047623+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/W5DUGWF4MIIXW2OQRWZ2U3GJN4","json":"https://pith.science/pith/W5DUGWF4MIIXW2OQRWZ2U3GJN4.json","graph_json":"https://pith.science/api/pith-number/W5DUGWF4MIIXW2OQRWZ2U3GJN4/graph.json","events_json":"https://pith.science/api/pith-number/W5DUGWF4MIIXW2OQRWZ2U3GJN4/events.json","paper":"https://pith.science/paper/W5DUGWF4"},"agent_actions":{"view_html":"https://pith.science/pith/W5DUGWF4MIIXW2OQRWZ2U3GJN4","download_json":"https://pith.science/pith/W5DUGWF4MIIXW2OQRWZ2U3GJN4.json","view_paper":"https://pith.science/paper/W5DUGWF4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.01241&json=true","fetch_graph":"https://pith.science/api/pith-number/W5DUGWF4MIIXW2OQRWZ2U3GJN4/graph.json","fetch_events":"https://pith.science/api/pith-number/W5DUGWF4MIIXW2OQRWZ2U3GJN4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W5DUGWF4MIIXW2OQRWZ2U3GJN4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W5DUGWF4MIIXW2OQRWZ2U3GJN4/action/storage_attestation","attest_author":"https://pith.science/pith/W5DUGWF4MIIXW2OQRWZ2U3GJN4/action/author_attestation","sign_citation":"https://pith.science/pith/W5DUGWF4MIIXW2OQRWZ2U3GJN4/action/citation_signature","submit_replication":"https://pith.science/pith/W5DUGWF4MIIXW2OQRWZ2U3GJN4/action/replication_record"}},"created_at":"2026-05-18T00:21:56.604227+00:00","updated_at":"2026-05-18T00:21:56.604227+00:00"}