{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:AIMS66I6IDGEWR2FY7C5JF7HL3","short_pith_number":"pith:AIMS66I6","schema_version":"1.0","canonical_sha256":"02192f791e40cc4b4745c7c5d497e75ef4cb15294dc1c939f8de1bf6b70627a4","source":{"kind":"arxiv","id":"2402.08802","version":1},"attestation_state":"computed","paper":{"title":"Multi-Label Zero-Shot Product Attribute-Value Extraction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Hoda Eldardiry, Jiaying Gong","submitted_at":"2024-02-13T21:19:00Z","abstract_excerpt":"E-commerce platforms should provide detailed product descriptions (attribute values) for effective product search and recommendation. However, attribute value information is typically not available for new products. To predict unseen attribute values, large quantities of labeled training data are needed to train a traditional supervised learning model. Typically, it is difficult, time-consuming, and costly to manually label large quantities of new product profiles. In this paper, we propose a novel method to efficiently and effectively extract unseen attribute values from new products in the a"},"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":"2402.08802","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-02-13T21:19:00Z","cross_cats_sorted":[],"title_canon_sha256":"30b7f4df78d7d6f1702400002e8979262cd72a41f7e3a158eccab8e3ef59597e","abstract_canon_sha256":"6edfc560e15e3be8b3f783ec707e36aa5a4abb1b7c1297e2a50cd4c6688ef3e4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:45:02.933008Z","signature_b64":"P5XpuPrjAdH70qbExR7ONshfWNKZxpMnQIb7HUQCuYf2cz+s+jtliEwfBBrXz8gQhlSFndMuSc5U66AbjCY8Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"02192f791e40cc4b4745c7c5d497e75ef4cb15294dc1c939f8de1bf6b70627a4","last_reissued_at":"2026-07-05T07:45:02.932528Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:45:02.932528Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Label Zero-Shot Product Attribute-Value Extraction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Hoda Eldardiry, Jiaying Gong","submitted_at":"2024-02-13T21:19:00Z","abstract_excerpt":"E-commerce platforms should provide detailed product descriptions (attribute values) for effective product search and recommendation. However, attribute value information is typically not available for new products. To predict unseen attribute values, large quantities of labeled training data are needed to train a traditional supervised learning model. Typically, it is difficult, time-consuming, and costly to manually label large quantities of new product profiles. In this paper, we propose a novel method to efficiently and effectively extract unseen attribute values from new products in the a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.08802","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2402.08802/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2402.08802","created_at":"2026-07-05T07:45:02.932590+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.08802v1","created_at":"2026-07-05T07:45:02.932590+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.08802","created_at":"2026-07-05T07:45:02.932590+00:00"},{"alias_kind":"pith_short_12","alias_value":"AIMS66I6IDGE","created_at":"2026-07-05T07:45:02.932590+00:00"},{"alias_kind":"pith_short_16","alias_value":"AIMS66I6IDGEWR2F","created_at":"2026-07-05T07:45:02.932590+00:00"},{"alias_kind":"pith_short_8","alias_value":"AIMS66I6","created_at":"2026-07-05T07:45:02.932590+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/AIMS66I6IDGEWR2FY7C5JF7HL3","json":"https://pith.science/pith/AIMS66I6IDGEWR2FY7C5JF7HL3.json","graph_json":"https://pith.science/api/pith-number/AIMS66I6IDGEWR2FY7C5JF7HL3/graph.json","events_json":"https://pith.science/api/pith-number/AIMS66I6IDGEWR2FY7C5JF7HL3/events.json","paper":"https://pith.science/paper/AIMS66I6"},"agent_actions":{"view_html":"https://pith.science/pith/AIMS66I6IDGEWR2FY7C5JF7HL3","download_json":"https://pith.science/pith/AIMS66I6IDGEWR2FY7C5JF7HL3.json","view_paper":"https://pith.science/paper/AIMS66I6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.08802&json=true","fetch_graph":"https://pith.science/api/pith-number/AIMS66I6IDGEWR2FY7C5JF7HL3/graph.json","fetch_events":"https://pith.science/api/pith-number/AIMS66I6IDGEWR2FY7C5JF7HL3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AIMS66I6IDGEWR2FY7C5JF7HL3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AIMS66I6IDGEWR2FY7C5JF7HL3/action/storage_attestation","attest_author":"https://pith.science/pith/AIMS66I6IDGEWR2FY7C5JF7HL3/action/author_attestation","sign_citation":"https://pith.science/pith/AIMS66I6IDGEWR2FY7C5JF7HL3/action/citation_signature","submit_replication":"https://pith.science/pith/AIMS66I6IDGEWR2FY7C5JF7HL3/action/replication_record"}},"created_at":"2026-07-05T07:45:02.932590+00:00","updated_at":"2026-07-05T07:45:02.932590+00:00"}