{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7TIFPEYVUIF77OXZ3LGBSNNHWA","short_pith_number":"pith:7TIFPEYV","schema_version":"1.0","canonical_sha256":"fcd0579315a20bffbaf9dacc1935a7b017dd6019ed1b2ec31117054ea0052189","source":{"kind":"arxiv","id":"2505.21040","version":2},"attestation_state":"computed","paper":{"title":"FCKT: Fine-Grained Cross-Task Knowledge Transfer with Semantic Contrastive Learning for Targeted Sentiment Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Fuzhen Zhuang, Kepeng Xu, Meng Yuan, Wei Chen, Zhao Zhang","submitted_at":"2025-05-27T11:23:53Z","abstract_excerpt":"In this paper, we address the task of targeted sentiment analysis (TSA), which involves two sub-tasks, i.e., identifying specific aspects from reviews and determining their corresponding sentiments. Aspect extraction forms the foundation for sentiment prediction, highlighting the critical dependency between these two tasks for effective cross-task knowledge transfer. While most existing studies adopt a multi-task learning paradigm to align task-specific features in the latent space, they predominantly rely on coarse-grained knowledge transfer. Such approaches lack fine-grained control over asp"},"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":"2505.21040","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-27T11:23:53Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a6a01b12c453d3e3baf9cca9a85b98d595fe6d1fd71bb8b35487d4b87ce6037d","abstract_canon_sha256":"8327697ec2bb9bde4fd5390f3f8b8e1a2f32323f2d41a204631e918681581241"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:10:58.859371Z","signature_b64":"WyAbVaLgqT34et+uuY+ZKsqvmbwq0FgAzs+LS6nhY6QaP9q+LR697ykn0zT9Fffnhx2P+osoCMV1gns3/1Y/CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fcd0579315a20bffbaf9dacc1935a7b017dd6019ed1b2ec31117054ea0052189","last_reissued_at":"2026-07-05T11:10:58.858793Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:10:58.858793Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FCKT: Fine-Grained Cross-Task Knowledge Transfer with Semantic Contrastive Learning for Targeted Sentiment Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Fuzhen Zhuang, Kepeng Xu, Meng Yuan, Wei Chen, Zhao Zhang","submitted_at":"2025-05-27T11:23:53Z","abstract_excerpt":"In this paper, we address the task of targeted sentiment analysis (TSA), which involves two sub-tasks, i.e., identifying specific aspects from reviews and determining their corresponding sentiments. Aspect extraction forms the foundation for sentiment prediction, highlighting the critical dependency between these two tasks for effective cross-task knowledge transfer. While most existing studies adopt a multi-task learning paradigm to align task-specific features in the latent space, they predominantly rely on coarse-grained knowledge transfer. Such approaches lack fine-grained control over asp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.21040","kind":"arxiv","version":2},"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/2505.21040/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":"2505.21040","created_at":"2026-07-05T11:10:58.858847+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.21040v2","created_at":"2026-07-05T11:10:58.858847+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.21040","created_at":"2026-07-05T11:10:58.858847+00:00"},{"alias_kind":"pith_short_12","alias_value":"7TIFPEYVUIF7","created_at":"2026-07-05T11:10:58.858847+00:00"},{"alias_kind":"pith_short_16","alias_value":"7TIFPEYVUIF77OXZ","created_at":"2026-07-05T11:10:58.858847+00:00"},{"alias_kind":"pith_short_8","alias_value":"7TIFPEYV","created_at":"2026-07-05T11:10:58.858847+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/7TIFPEYVUIF77OXZ3LGBSNNHWA","json":"https://pith.science/pith/7TIFPEYVUIF77OXZ3LGBSNNHWA.json","graph_json":"https://pith.science/api/pith-number/7TIFPEYVUIF77OXZ3LGBSNNHWA/graph.json","events_json":"https://pith.science/api/pith-number/7TIFPEYVUIF77OXZ3LGBSNNHWA/events.json","paper":"https://pith.science/paper/7TIFPEYV"},"agent_actions":{"view_html":"https://pith.science/pith/7TIFPEYVUIF77OXZ3LGBSNNHWA","download_json":"https://pith.science/pith/7TIFPEYVUIF77OXZ3LGBSNNHWA.json","view_paper":"https://pith.science/paper/7TIFPEYV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.21040&json=true","fetch_graph":"https://pith.science/api/pith-number/7TIFPEYVUIF77OXZ3LGBSNNHWA/graph.json","fetch_events":"https://pith.science/api/pith-number/7TIFPEYVUIF77OXZ3LGBSNNHWA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7TIFPEYVUIF77OXZ3LGBSNNHWA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7TIFPEYVUIF77OXZ3LGBSNNHWA/action/storage_attestation","attest_author":"https://pith.science/pith/7TIFPEYVUIF77OXZ3LGBSNNHWA/action/author_attestation","sign_citation":"https://pith.science/pith/7TIFPEYVUIF77OXZ3LGBSNNHWA/action/citation_signature","submit_replication":"https://pith.science/pith/7TIFPEYVUIF77OXZ3LGBSNNHWA/action/replication_record"}},"created_at":"2026-07-05T11:10:58.858847+00:00","updated_at":"2026-07-05T11:10:58.858847+00:00"}