{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:KQD54TH6LX5IVLZMWYTQEUWVOA","short_pith_number":"pith:KQD54TH6","schema_version":"1.0","canonical_sha256":"5407de4cfe5dfa8aaf2cb6270252d57021a5d576df72381ef7367049bddb14c1","source":{"kind":"arxiv","id":"1704.03223","version":1},"attestation_state":"computed","paper":{"title":"Persian Wordnet Construction using Supervised Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CL","authors_text":"Heshaam Faili, Zahra Mousavi","submitted_at":"2017-04-11T09:47:28Z","abstract_excerpt":"This paper presents an automated supervised method for Persian wordnet construction. Using a Persian corpus and a bi-lingual dictionary, the initial links between Persian words and Princeton WordNet synsets have been generated. These links will be discriminated later as correct or incorrect by employing seven features in a trained classification system. The whole method is just a classification system, which has been trained on a train set containing FarsNet as a set of correct instances. State of the art results on the automatically derived Persian wordnet is achieved. The resulted wordnet wi"},"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":"1704.03223","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-11T09:47:28Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"f4fb67a182d6c6fd5488f0f860077abd804d27e69a9c0470918386cd950c0381","abstract_canon_sha256":"d2f872045b5d8c48d191bb10583b321e5b6ca6b4d713df7487d56a1654964ed2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:46:33.140671Z","signature_b64":"/nrbETTZ9XHEifJzw9NpZVsjAmihrmlNoqyO98PzLW4OwPkC2scdBp9W2LfrAlk9L+3uhmm6yeEosKDv9S9ZCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5407de4cfe5dfa8aaf2cb6270252d57021a5d576df72381ef7367049bddb14c1","last_reissued_at":"2026-05-18T00:46:33.139958Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:46:33.139958Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Persian Wordnet Construction using Supervised Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CL","authors_text":"Heshaam Faili, Zahra Mousavi","submitted_at":"2017-04-11T09:47:28Z","abstract_excerpt":"This paper presents an automated supervised method for Persian wordnet construction. Using a Persian corpus and a bi-lingual dictionary, the initial links between Persian words and Princeton WordNet synsets have been generated. These links will be discriminated later as correct or incorrect by employing seven features in a trained classification system. The whole method is just a classification system, which has been trained on a train set containing FarsNet as a set of correct instances. State of the art results on the automatically derived Persian wordnet is achieved. The resulted wordnet wi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.03223","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":"1704.03223","created_at":"2026-05-18T00:46:33.140066+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.03223v1","created_at":"2026-05-18T00:46:33.140066+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.03223","created_at":"2026-05-18T00:46:33.140066+00:00"},{"alias_kind":"pith_short_12","alias_value":"KQD54TH6LX5I","created_at":"2026-05-18T12:31:24.725408+00:00"},{"alias_kind":"pith_short_16","alias_value":"KQD54TH6LX5IVLZM","created_at":"2026-05-18T12:31:24.725408+00:00"},{"alias_kind":"pith_short_8","alias_value":"KQD54TH6","created_at":"2026-05-18T12:31:24.725408+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/KQD54TH6LX5IVLZMWYTQEUWVOA","json":"https://pith.science/pith/KQD54TH6LX5IVLZMWYTQEUWVOA.json","graph_json":"https://pith.science/api/pith-number/KQD54TH6LX5IVLZMWYTQEUWVOA/graph.json","events_json":"https://pith.science/api/pith-number/KQD54TH6LX5IVLZMWYTQEUWVOA/events.json","paper":"https://pith.science/paper/KQD54TH6"},"agent_actions":{"view_html":"https://pith.science/pith/KQD54TH6LX5IVLZMWYTQEUWVOA","download_json":"https://pith.science/pith/KQD54TH6LX5IVLZMWYTQEUWVOA.json","view_paper":"https://pith.science/paper/KQD54TH6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.03223&json=true","fetch_graph":"https://pith.science/api/pith-number/KQD54TH6LX5IVLZMWYTQEUWVOA/graph.json","fetch_events":"https://pith.science/api/pith-number/KQD54TH6LX5IVLZMWYTQEUWVOA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KQD54TH6LX5IVLZMWYTQEUWVOA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KQD54TH6LX5IVLZMWYTQEUWVOA/action/storage_attestation","attest_author":"https://pith.science/pith/KQD54TH6LX5IVLZMWYTQEUWVOA/action/author_attestation","sign_citation":"https://pith.science/pith/KQD54TH6LX5IVLZMWYTQEUWVOA/action/citation_signature","submit_replication":"https://pith.science/pith/KQD54TH6LX5IVLZMWYTQEUWVOA/action/replication_record"}},"created_at":"2026-05-18T00:46:33.140066+00:00","updated_at":"2026-05-18T00:46:33.140066+00:00"}