{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:Q7LCO77DWHZYFGAQPAUAA5U7B7","short_pith_number":"pith:Q7LCO77D","canonical_record":{"source":{"id":"1910.06954","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-10-15T17:51:01Z","cross_cats_sorted":["cs.IR","cs.LG"],"title_canon_sha256":"166d4c419fa7eaaae43e0137a7512f647bf5dfb094a4bdee01f506fe7d243768","abstract_canon_sha256":"1a58342551c6f724884ca7ed07aa9179d41b2828bef14c5c7ea6de2c142e92e8"},"schema_version":"1.0"},"canonical_sha256":"87d6277fe3b1f3829810782800769f0ff9427281d399878ce3a4d7bf7e3eea2e","source":{"kind":"arxiv","id":"1910.06954","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.06954","created_at":"2026-07-05T01:19:36Z"},{"alias_kind":"arxiv_version","alias_value":"1910.06954v3","created_at":"2026-07-05T01:19:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.06954","created_at":"2026-07-05T01:19:36Z"},{"alias_kind":"pith_short_12","alias_value":"Q7LCO77DWHZY","created_at":"2026-07-05T01:19:36Z"},{"alias_kind":"pith_short_16","alias_value":"Q7LCO77DWHZYFGAQ","created_at":"2026-07-05T01:19:36Z"},{"alias_kind":"pith_short_8","alias_value":"Q7LCO77D","created_at":"2026-07-05T01:19:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:Q7LCO77DWHZYFGAQPAUAA5U7B7","target":"record","payload":{"canonical_record":{"source":{"id":"1910.06954","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-10-15T17:51:01Z","cross_cats_sorted":["cs.IR","cs.LG"],"title_canon_sha256":"166d4c419fa7eaaae43e0137a7512f647bf5dfb094a4bdee01f506fe7d243768","abstract_canon_sha256":"1a58342551c6f724884ca7ed07aa9179d41b2828bef14c5c7ea6de2c142e92e8"},"schema_version":"1.0"},"canonical_sha256":"87d6277fe3b1f3829810782800769f0ff9427281d399878ce3a4d7bf7e3eea2e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:19:36.736241Z","signature_b64":"/7GRh+yZO9RmrdKxd7FND0r49SmD2XBtGdd+uU+RmDRS/Iw10tjLzww1fLNMrYNBv+ZJ1q9qXcrFXuAZuyozCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"87d6277fe3b1f3829810782800769f0ff9427281d399878ce3a4d7bf7e3eea2e","last_reissued_at":"2026-07-05T01:19:36.735621Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:19:36.735621Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1910.06954","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:19:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"m2m1AnDBArXmR/w+5IFsH6yoeiBeDFaPFnlZ0+d1AyJo3Z/A7AwdKCiw2fsXx23aZCDBqLVlio3B2s51c33CBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T11:17:01.274207Z"},"content_sha256":"775f92b9f1e69213789467de86d76e6916d72a461a4f5493693f9f3960b15f36","schema_version":"1.0","event_id":"sha256:775f92b9f1e69213789467de86d76e6916d72a461a4f5493693f9f3960b15f36"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:Q7LCO77DWHZYFGAQPAUAA5U7B7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Context Matters: Recovering Human Semantic Structure from Machine Learning Analysis of Large-Scale Text Corpora","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","cs.LG"],"primary_cat":"cs.CL","authors_text":"Cameron T. Ellis, Jonathan D. Cohen, Marius C\\u{a}t\\u{a}lin Iordan, Nicole M. Beckage, Tyler Giallanza","submitted_at":"2019-10-15T17:51:01Z","abstract_excerpt":"Applying machine learning algorithms to large-scale, text-based corpora (embeddings) presents a unique opportunity to investigate at scale how human semantic knowledge is organized and how people use it to judge fundamental relationships, such as similarity between concepts. However, efforts to date have shown a substantial discrepancy between algorithm predictions and empirical judgments. Here, we introduce a novel approach of generating embeddings motivated by the psychological theory that semantic context plays a critical role in human judgments. Specifically, we train state-of-the-art mach"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.06954","kind":"arxiv","version":3},"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/1910.06954/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:19:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Fqk+52EjBjgIkKEHDZmRHImhBo5Bgvx21G9tSOuOcrU5oRRJ+k8iSxImhKIn25L5tURT8YHbLOri2QRU2nbgAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T11:17:01.274601Z"},"content_sha256":"0decea966a5136415e396aba45c91c83ee40bbc812d0accf15adb545d72e7fe6","schema_version":"1.0","event_id":"sha256:0decea966a5136415e396aba45c91c83ee40bbc812d0accf15adb545d72e7fe6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Q7LCO77DWHZYFGAQPAUAA5U7B7/bundle.json","state_url":"https://pith.science/pith/Q7LCO77DWHZYFGAQPAUAA5U7B7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Q7LCO77DWHZYFGAQPAUAA5U7B7/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-07T11:17:01Z","links":{"resolver":"https://pith.science/pith/Q7LCO77DWHZYFGAQPAUAA5U7B7","bundle":"https://pith.science/pith/Q7LCO77DWHZYFGAQPAUAA5U7B7/bundle.json","state":"https://pith.science/pith/Q7LCO77DWHZYFGAQPAUAA5U7B7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Q7LCO77DWHZYFGAQPAUAA5U7B7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:Q7LCO77DWHZYFGAQPAUAA5U7B7","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1a58342551c6f724884ca7ed07aa9179d41b2828bef14c5c7ea6de2c142e92e8","cross_cats_sorted":["cs.IR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-10-15T17:51:01Z","title_canon_sha256":"166d4c419fa7eaaae43e0137a7512f647bf5dfb094a4bdee01f506fe7d243768"},"schema_version":"1.0","source":{"id":"1910.06954","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.06954","created_at":"2026-07-05T01:19:36Z"},{"alias_kind":"arxiv_version","alias_value":"1910.06954v3","created_at":"2026-07-05T01:19:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.06954","created_at":"2026-07-05T01:19:36Z"},{"alias_kind":"pith_short_12","alias_value":"Q7LCO77DWHZY","created_at":"2026-07-05T01:19:36Z"},{"alias_kind":"pith_short_16","alias_value":"Q7LCO77DWHZYFGAQ","created_at":"2026-07-05T01:19:36Z"},{"alias_kind":"pith_short_8","alias_value":"Q7LCO77D","created_at":"2026-07-05T01:19:36Z"}],"graph_snapshots":[{"event_id":"sha256:0decea966a5136415e396aba45c91c83ee40bbc812d0accf15adb545d72e7fe6","target":"graph","created_at":"2026-07-05T01:19:36Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1910.06954/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Applying machine learning algorithms to large-scale, text-based corpora (embeddings) presents a unique opportunity to investigate at scale how human semantic knowledge is organized and how people use it to judge fundamental relationships, such as similarity between concepts. However, efforts to date have shown a substantial discrepancy between algorithm predictions and empirical judgments. Here, we introduce a novel approach of generating embeddings motivated by the psychological theory that semantic context plays a critical role in human judgments. Specifically, we train state-of-the-art mach","authors_text":"Cameron T. Ellis, Jonathan D. Cohen, Marius C\\u{a}t\\u{a}lin Iordan, Nicole M. Beckage, Tyler Giallanza","cross_cats":["cs.IR","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-10-15T17:51:01Z","title":"Context Matters: Recovering Human Semantic Structure from Machine Learning Analysis of Large-Scale Text Corpora"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.06954","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:775f92b9f1e69213789467de86d76e6916d72a461a4f5493693f9f3960b15f36","target":"record","created_at":"2026-07-05T01:19:36Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"1a58342551c6f724884ca7ed07aa9179d41b2828bef14c5c7ea6de2c142e92e8","cross_cats_sorted":["cs.IR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-10-15T17:51:01Z","title_canon_sha256":"166d4c419fa7eaaae43e0137a7512f647bf5dfb094a4bdee01f506fe7d243768"},"schema_version":"1.0","source":{"id":"1910.06954","kind":"arxiv","version":3}},"canonical_sha256":"87d6277fe3b1f3829810782800769f0ff9427281d399878ce3a4d7bf7e3eea2e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"87d6277fe3b1f3829810782800769f0ff9427281d399878ce3a4d7bf7e3eea2e","first_computed_at":"2026-07-05T01:19:36.735621Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:19:36.735621Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/7GRh+yZO9RmrdKxd7FND0r49SmD2XBtGdd+uU+RmDRS/Iw10tjLzww1fLNMrYNBv+ZJ1q9qXcrFXuAZuyozCA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:19:36.736241Z","signed_message":"canonical_sha256_bytes"},"source_id":"1910.06954","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:775f92b9f1e69213789467de86d76e6916d72a461a4f5493693f9f3960b15f36","sha256:0decea966a5136415e396aba45c91c83ee40bbc812d0accf15adb545d72e7fe6"],"state_sha256":"9c26d2c2088ea83266a58331c988a30c80d0ebb760bb030796655c1ce77c504b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8s6YXS1WTStyJz2FVLcoZhz7fi69b1Yz7rEPnKWrRL0qGim/UQFJu/pjWK9JNcwqaEotv+xDwkryAKzacfEYAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T11:17:01.276709Z","bundle_sha256":"f34a79895ad638543329abdd10a601d8f740ea9d1401097090891b4e17444b5a"}}