{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:UGSNNNLHFVS7T7EM6HFNELUWAL","short_pith_number":"pith:UGSNNNLH","canonical_record":{"source":{"id":"1704.04550","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-14T22:29:27Z","cross_cats_sorted":[],"title_canon_sha256":"b7bfce6f9a2ff6579030f683a9ecd165154c9f1ac10b10c6aeff9e30f033e87b","abstract_canon_sha256":"750be00be0826d7b5c89cb65ee0b6227973a2ac3865e7a505e9c1ceb4eb2f570"},"schema_version":"1.0"},"canonical_sha256":"a1a4d6b5672d65f9fc8cf1cad22e9602e5c6b78e44d8070c3fce0f43b9695c5d","source":{"kind":"arxiv","id":"1704.04550","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.04550","created_at":"2026-05-18T00:32:59Z"},{"alias_kind":"arxiv_version","alias_value":"1704.04550v4","created_at":"2026-05-18T00:32:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.04550","created_at":"2026-05-18T00:32:59Z"},{"alias_kind":"pith_short_12","alias_value":"UGSNNNLHFVS7","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"UGSNNNLHFVS7T7EM","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"UGSNNNLH","created_at":"2026-05-18T12:31:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:UGSNNNLHFVS7T7EM6HFNELUWAL","target":"record","payload":{"canonical_record":{"source":{"id":"1704.04550","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-14T22:29:27Z","cross_cats_sorted":[],"title_canon_sha256":"b7bfce6f9a2ff6579030f683a9ecd165154c9f1ac10b10c6aeff9e30f033e87b","abstract_canon_sha256":"750be00be0826d7b5c89cb65ee0b6227973a2ac3865e7a505e9c1ceb4eb2f570"},"schema_version":"1.0"},"canonical_sha256":"a1a4d6b5672d65f9fc8cf1cad22e9602e5c6b78e44d8070c3fce0f43b9695c5d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:59.756168Z","signature_b64":"09Ps1kIeErr6ECtg+5obnTApVUyiUIFymixUW2VmV1sgp8MUoD8ezWePSFejgtDzX2dqEZfvIagOMGzovqI2Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a1a4d6b5672d65f9fc8cf1cad22e9602e5c6b78e44d8070c3fce0f43b9695c5d","last_reissued_at":"2026-05-18T00:32:59.755548Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:59.755548Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1704.04550","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:32:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kT1fluy5gdrwLc7f8q/c/v7fa++8EhMucAyaKHKMz4tmOD6ypdiopK81EIR/hULbqbXmw9HuSfhkzpeooL6CDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T01:29:04.541786Z"},"content_sha256":"88623c3260839a21c16e99665ad16419af3d6e7c1399daad7bcfe886805cacb6","schema_version":"1.0","event_id":"sha256:88623c3260839a21c16e99665ad16419af3d6e7c1399daad7bcfe886805cacb6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:UGSNNNLHFVS7T7EM6HFNELUWAL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Distributional Modeling on a Diet: One-shot Word Learning from Text Only","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Katrin Erk, Stephen Roller, Su Wang","submitted_at":"2017-04-14T22:29:27Z","abstract_excerpt":"We test whether distributional models can do one-shot learning of definitional properties from text only. Using Bayesian models, we find that first learning overarching structure in the known data, regularities in textual contexts and in properties, helps one-shot learning, and that individual context items can be highly informative. Our experiments show that our model can learn properties from a single exposure when given an informative utterance."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.04550","kind":"arxiv","version":4},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:32:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"adwAST6KXTuI6b6fWtid78ON/A7rJclQqAmRB7vWUrl9nN05bAtiwLj7wxlWNuxc4BOvrfJeUHFC2hY84FqIAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T01:29:04.542128Z"},"content_sha256":"721c2315ff96b8b2b1c806b24f1d1d98f01222c8d50ea4e6c5edbe18be0ba786","schema_version":"1.0","event_id":"sha256:721c2315ff96b8b2b1c806b24f1d1d98f01222c8d50ea4e6c5edbe18be0ba786"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UGSNNNLHFVS7T7EM6HFNELUWAL/bundle.json","state_url":"https://pith.science/pith/UGSNNNLHFVS7T7EM6HFNELUWAL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UGSNNNLHFVS7T7EM6HFNELUWAL/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":"2026-06-04T01:29:04Z","links":{"resolver":"https://pith.science/pith/UGSNNNLHFVS7T7EM6HFNELUWAL","bundle":"https://pith.science/pith/UGSNNNLHFVS7T7EM6HFNELUWAL/bundle.json","state":"https://pith.science/pith/UGSNNNLHFVS7T7EM6HFNELUWAL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UGSNNNLHFVS7T7EM6HFNELUWAL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:UGSNNNLHFVS7T7EM6HFNELUWAL","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":"750be00be0826d7b5c89cb65ee0b6227973a2ac3865e7a505e9c1ceb4eb2f570","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-14T22:29:27Z","title_canon_sha256":"b7bfce6f9a2ff6579030f683a9ecd165154c9f1ac10b10c6aeff9e30f033e87b"},"schema_version":"1.0","source":{"id":"1704.04550","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.04550","created_at":"2026-05-18T00:32:59Z"},{"alias_kind":"arxiv_version","alias_value":"1704.04550v4","created_at":"2026-05-18T00:32:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.04550","created_at":"2026-05-18T00:32:59Z"},{"alias_kind":"pith_short_12","alias_value":"UGSNNNLHFVS7","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"UGSNNNLHFVS7T7EM","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"UGSNNNLH","created_at":"2026-05-18T12:31:46Z"}],"graph_snapshots":[{"event_id":"sha256:721c2315ff96b8b2b1c806b24f1d1d98f01222c8d50ea4e6c5edbe18be0ba786","target":"graph","created_at":"2026-05-18T00:32:59Z","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"},"paper":{"abstract_excerpt":"We test whether distributional models can do one-shot learning of definitional properties from text only. Using Bayesian models, we find that first learning overarching structure in the known data, regularities in textual contexts and in properties, helps one-shot learning, and that individual context items can be highly informative. Our experiments show that our model can learn properties from a single exposure when given an informative utterance.","authors_text":"Katrin Erk, Stephen Roller, Su Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-14T22:29:27Z","title":"Distributional Modeling on a Diet: One-shot Word Learning from Text Only"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.04550","kind":"arxiv","version":4},"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":"sha256:88623c3260839a21c16e99665ad16419af3d6e7c1399daad7bcfe886805cacb6","target":"record","created_at":"2026-05-18T00:32:59Z","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":"750be00be0826d7b5c89cb65ee0b6227973a2ac3865e7a505e9c1ceb4eb2f570","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-14T22:29:27Z","title_canon_sha256":"b7bfce6f9a2ff6579030f683a9ecd165154c9f1ac10b10c6aeff9e30f033e87b"},"schema_version":"1.0","source":{"id":"1704.04550","kind":"arxiv","version":4}},"canonical_sha256":"a1a4d6b5672d65f9fc8cf1cad22e9602e5c6b78e44d8070c3fce0f43b9695c5d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a1a4d6b5672d65f9fc8cf1cad22e9602e5c6b78e44d8070c3fce0f43b9695c5d","first_computed_at":"2026-05-18T00:32:59.755548Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:32:59.755548Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"09Ps1kIeErr6ECtg+5obnTApVUyiUIFymixUW2VmV1sgp8MUoD8ezWePSFejgtDzX2dqEZfvIagOMGzovqI2Cg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:32:59.756168Z","signed_message":"canonical_sha256_bytes"},"source_id":"1704.04550","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:88623c3260839a21c16e99665ad16419af3d6e7c1399daad7bcfe886805cacb6","sha256:721c2315ff96b8b2b1c806b24f1d1d98f01222c8d50ea4e6c5edbe18be0ba786"],"state_sha256":"daf0e9e26745267caa27b4ab13384033a5d87ff7e32ae8ffef4c3e49c28db729"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NFLmywWyMURPdxKhkZBPhvzbXK2diiIQ1zv+nSRxqw/twmfYibBzl3EvN+cCwL3np/zs8lPBSDC8SL8SVKBGCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T01:29:04.544443Z","bundle_sha256":"25ad5a737f19202e74394bf3cca8ac1f69c2934e29fb2026d193c1793c997acc"}}