{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:BYA426LY2W7FQVCKWJAMDXCIPT","short_pith_number":"pith:BYA426LY","schema_version":"1.0","canonical_sha256":"0e01cd7978d5be58544ab240c1dc487cdf60cc43eeb12919133237621df2b70e","source":{"kind":"arxiv","id":"1804.09843","version":1},"attestation_state":"computed","paper":{"title":"Hierarchical Density Order Embeddings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","stat.ML"],"primary_cat":"cs.CL","authors_text":"Andrew Gordon Wilson, Ben Athiwaratkun","submitted_at":"2018-04-26T00:43:49Z","abstract_excerpt":"By representing words with probability densities rather than point vectors, probabilistic word embeddings can capture rich and interpretable semantic information and uncertainty. The uncertainty information can be particularly meaningful in capturing entailment relationships -- whereby general words such as \"entity\" correspond to broad distributions that encompass more specific words such as \"animal\" or \"instrument\". We introduce density order embeddings, which learn hierarchical representations through encapsulation of probability densities. In particular, we propose simple yet effective loss"},"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":"1804.09843","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-04-26T00:43:49Z","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"title_canon_sha256":"19b3b5dc42d5c05772d98b9b6bd58fe172b92e9bb5aa432c457e9a4534f31cd5","abstract_canon_sha256":"ffa26093a7c681ebbd2cef49142cfaece727caa8d1487fcf8b5efcb2f88b8496"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:17:23.519231Z","signature_b64":"oBmKLT5OeR+fjmmA0s4dcxzcfVLxjDWA4TbY0J0EqcnzLT4lkIAoAvp4DNY9psPyCrdMTYcuPs2ZqpiPxghdBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0e01cd7978d5be58544ab240c1dc487cdf60cc43eeb12919133237621df2b70e","last_reissued_at":"2026-05-18T00:17:23.518464Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:17:23.518464Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hierarchical Density Order Embeddings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","stat.ML"],"primary_cat":"cs.CL","authors_text":"Andrew Gordon Wilson, Ben Athiwaratkun","submitted_at":"2018-04-26T00:43:49Z","abstract_excerpt":"By representing words with probability densities rather than point vectors, probabilistic word embeddings can capture rich and interpretable semantic information and uncertainty. The uncertainty information can be particularly meaningful in capturing entailment relationships -- whereby general words such as \"entity\" correspond to broad distributions that encompass more specific words such as \"animal\" or \"instrument\". We introduce density order embeddings, which learn hierarchical representations through encapsulation of probability densities. In particular, we propose simple yet effective loss"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.09843","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":"1804.09843","created_at":"2026-05-18T00:17:23.518608+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.09843v1","created_at":"2026-05-18T00:17:23.518608+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.09843","created_at":"2026-05-18T00:17:23.518608+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/BYA426LY2W7FQVCKWJAMDXCIPT","json":"https://pith.science/pith/BYA426LY2W7FQVCKWJAMDXCIPT.json","graph_json":"https://pith.science/api/pith-number/BYA426LY2W7FQVCKWJAMDXCIPT/graph.json","events_json":"https://pith.science/api/pith-number/BYA426LY2W7FQVCKWJAMDXCIPT/events.json","paper":"https://pith.science/paper/BYA426LY"},"agent_actions":{"view_html":"https://pith.science/pith/BYA426LY2W7FQVCKWJAMDXCIPT","download_json":"https://pith.science/pith/BYA426LY2W7FQVCKWJAMDXCIPT.json","view_paper":"https://pith.science/paper/BYA426LY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.09843&json=true","fetch_graph":"https://pith.science/api/pith-number/BYA426LY2W7FQVCKWJAMDXCIPT/graph.json","fetch_events":"https://pith.science/api/pith-number/BYA426LY2W7FQVCKWJAMDXCIPT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BYA426LY2W7FQVCKWJAMDXCIPT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BYA426LY2W7FQVCKWJAMDXCIPT/action/storage_attestation","attest_author":"https://pith.science/pith/BYA426LY2W7FQVCKWJAMDXCIPT/action/author_attestation","sign_citation":"https://pith.science/pith/BYA426LY2W7FQVCKWJAMDXCIPT/action/citation_signature","submit_replication":"https://pith.science/pith/BYA426LY2W7FQVCKWJAMDXCIPT/action/replication_record"}},"created_at":"2026-05-18T00:17:23.518608+00:00","updated_at":"2026-05-18T00:17:23.518608+00:00"}