{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:TCALPDFWYXOMBLMCV6UMCDHR3U","short_pith_number":"pith:TCALPDFW","schema_version":"1.0","canonical_sha256":"9880b78cb6c5dcc0ad82afa8c10cf1dd189db4233916fc5fa4c63073af0eb70d","source":{"kind":"arxiv","id":"1809.04163","version":1},"attestation_state":"computed","paper":{"title":"Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Anna Korhonen, Edoardo Maria Ponti, Goran Glava\\v{s}, Ivan Vuli\\'c, Nikola Mrk\\v{s}i\\'c","submitted_at":"2018-09-11T21:08:00Z","abstract_excerpt":"Semantic specialization is the process of fine-tuning pre-trained distributional word vectors using external lexical knowledge (e.g., WordNet) to accentuate a particular semantic relation in the specialized vector space. While post-processing specialization methods are applicable to arbitrary distributional vectors, they are limited to updating only the vectors of words occurring in external lexicons (i.e., seen words), leaving the vectors of all other words unchanged. We propose a novel approach to specializing the full distributional vocabulary. Our adversarial post-specialization method pro"},"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":"1809.04163","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-09-11T21:08:00Z","cross_cats_sorted":[],"title_canon_sha256":"0c0e0ba3ebbb2f731e4a6bd2fbb965e0a7e79f2609bcf838ac99331a25065995","abstract_canon_sha256":"682faee65a6df7804e5239de13cdfbd5bf442373205171d87a42110f1c8476a1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:47.019137Z","signature_b64":"jUBraOv6DYMyccn7adr8hq0/qD/lGlvOSY6WrpTKQIWGa8+bs3E0YAkk2IXU/pKCs50+lPTGG8laLIcFWnZXCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9880b78cb6c5dcc0ad82afa8c10cf1dd189db4233916fc5fa4c63073af0eb70d","last_reissued_at":"2026-05-18T00:01:47.018615Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:47.018615Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Anna Korhonen, Edoardo Maria Ponti, Goran Glava\\v{s}, Ivan Vuli\\'c, Nikola Mrk\\v{s}i\\'c","submitted_at":"2018-09-11T21:08:00Z","abstract_excerpt":"Semantic specialization is the process of fine-tuning pre-trained distributional word vectors using external lexical knowledge (e.g., WordNet) to accentuate a particular semantic relation in the specialized vector space. While post-processing specialization methods are applicable to arbitrary distributional vectors, they are limited to updating only the vectors of words occurring in external lexicons (i.e., seen words), leaving the vectors of all other words unchanged. We propose a novel approach to specializing the full distributional vocabulary. Our adversarial post-specialization method pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.04163","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":"1809.04163","created_at":"2026-05-18T00:01:47.018702+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.04163v1","created_at":"2026-05-18T00:01:47.018702+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.04163","created_at":"2026-05-18T00:01:47.018702+00:00"},{"alias_kind":"pith_short_12","alias_value":"TCALPDFWYXOM","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_16","alias_value":"TCALPDFWYXOMBLMC","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_8","alias_value":"TCALPDFW","created_at":"2026-05-18T12:32:53.628368+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/TCALPDFWYXOMBLMCV6UMCDHR3U","json":"https://pith.science/pith/TCALPDFWYXOMBLMCV6UMCDHR3U.json","graph_json":"https://pith.science/api/pith-number/TCALPDFWYXOMBLMCV6UMCDHR3U/graph.json","events_json":"https://pith.science/api/pith-number/TCALPDFWYXOMBLMCV6UMCDHR3U/events.json","paper":"https://pith.science/paper/TCALPDFW"},"agent_actions":{"view_html":"https://pith.science/pith/TCALPDFWYXOMBLMCV6UMCDHR3U","download_json":"https://pith.science/pith/TCALPDFWYXOMBLMCV6UMCDHR3U.json","view_paper":"https://pith.science/paper/TCALPDFW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.04163&json=true","fetch_graph":"https://pith.science/api/pith-number/TCALPDFWYXOMBLMCV6UMCDHR3U/graph.json","fetch_events":"https://pith.science/api/pith-number/TCALPDFWYXOMBLMCV6UMCDHR3U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TCALPDFWYXOMBLMCV6UMCDHR3U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TCALPDFWYXOMBLMCV6UMCDHR3U/action/storage_attestation","attest_author":"https://pith.science/pith/TCALPDFWYXOMBLMCV6UMCDHR3U/action/author_attestation","sign_citation":"https://pith.science/pith/TCALPDFWYXOMBLMCV6UMCDHR3U/action/citation_signature","submit_replication":"https://pith.science/pith/TCALPDFWYXOMBLMCV6UMCDHR3U/action/replication_record"}},"created_at":"2026-05-18T00:01:47.018702+00:00","updated_at":"2026-05-18T00:01:47.018702+00:00"}