{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:BRRQMRQFKVY47VTYL4KLHOSMRG","short_pith_number":"pith:BRRQMRQF","schema_version":"1.0","canonical_sha256":"0c630646055571cfd6785f14b3ba4c89a58a58204de9d97208101ac597284017","source":{"kind":"arxiv","id":"2504.16856","version":1},"attestation_state":"computed","paper":{"title":"Emo Pillars: Knowledge Distillation to Support Fine-Grained Context-Aware and Context-Less Emotion Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alexander Shvets","submitted_at":"2025-04-23T16:23:17Z","abstract_excerpt":"Most datasets for sentiment analysis lack context in which an opinion was expressed, often crucial for emotion understanding, and are mainly limited by a few emotion categories. Foundation large language models (LLMs) like GPT-4 suffer from over-predicting emotions and are too resource-intensive. We design an LLM-based data synthesis pipeline and leverage a large model, Mistral-7b, for the generation of training examples for more accessible, lightweight BERT-type encoder models. We focus on enlarging the semantic diversity of examples and propose grounding the generation into a corpus of narra"},"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":"2504.16856","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-23T16:23:17Z","cross_cats_sorted":[],"title_canon_sha256":"79be93da7c1480dc3e3c7dae34760865acb57ae8b14f2a6d48339532669a5d1d","abstract_canon_sha256":"ba28ef808b2ce566057f6f038bdd006b537320bffdbc7d65c3ae2dc6c89e7452"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:53:05.517061Z","signature_b64":"Ah20MsemCerE0EXrny3cXI7t31byaFg5kMmcvhyR36tCLurtGP8BCWAohlaYoVsmcj6/gsib/X4QPRGLH1RnBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c630646055571cfd6785f14b3ba4c89a58a58204de9d97208101ac597284017","last_reissued_at":"2026-07-05T10:53:05.516566Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:53:05.516566Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Emo Pillars: Knowledge Distillation to Support Fine-Grained Context-Aware and Context-Less Emotion Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alexander Shvets","submitted_at":"2025-04-23T16:23:17Z","abstract_excerpt":"Most datasets for sentiment analysis lack context in which an opinion was expressed, often crucial for emotion understanding, and are mainly limited by a few emotion categories. Foundation large language models (LLMs) like GPT-4 suffer from over-predicting emotions and are too resource-intensive. We design an LLM-based data synthesis pipeline and leverage a large model, Mistral-7b, for the generation of training examples for more accessible, lightweight BERT-type encoder models. We focus on enlarging the semantic diversity of examples and propose grounding the generation into a corpus of narra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.16856","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/2504.16856/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":"2504.16856","created_at":"2026-07-05T10:53:05.516626+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.16856v1","created_at":"2026-07-05T10:53:05.516626+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.16856","created_at":"2026-07-05T10:53:05.516626+00:00"},{"alias_kind":"pith_short_12","alias_value":"BRRQMRQFKVY4","created_at":"2026-07-05T10:53:05.516626+00:00"},{"alias_kind":"pith_short_16","alias_value":"BRRQMRQFKVY47VTY","created_at":"2026-07-05T10:53:05.516626+00:00"},{"alias_kind":"pith_short_8","alias_value":"BRRQMRQF","created_at":"2026-07-05T10:53:05.516626+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/BRRQMRQFKVY47VTYL4KLHOSMRG","json":"https://pith.science/pith/BRRQMRQFKVY47VTYL4KLHOSMRG.json","graph_json":"https://pith.science/api/pith-number/BRRQMRQFKVY47VTYL4KLHOSMRG/graph.json","events_json":"https://pith.science/api/pith-number/BRRQMRQFKVY47VTYL4KLHOSMRG/events.json","paper":"https://pith.science/paper/BRRQMRQF"},"agent_actions":{"view_html":"https://pith.science/pith/BRRQMRQFKVY47VTYL4KLHOSMRG","download_json":"https://pith.science/pith/BRRQMRQFKVY47VTYL4KLHOSMRG.json","view_paper":"https://pith.science/paper/BRRQMRQF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.16856&json=true","fetch_graph":"https://pith.science/api/pith-number/BRRQMRQFKVY47VTYL4KLHOSMRG/graph.json","fetch_events":"https://pith.science/api/pith-number/BRRQMRQFKVY47VTYL4KLHOSMRG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BRRQMRQFKVY47VTYL4KLHOSMRG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BRRQMRQFKVY47VTYL4KLHOSMRG/action/storage_attestation","attest_author":"https://pith.science/pith/BRRQMRQFKVY47VTYL4KLHOSMRG/action/author_attestation","sign_citation":"https://pith.science/pith/BRRQMRQFKVY47VTYL4KLHOSMRG/action/citation_signature","submit_replication":"https://pith.science/pith/BRRQMRQFKVY47VTYL4KLHOSMRG/action/replication_record"}},"created_at":"2026-07-05T10:53:05.516626+00:00","updated_at":"2026-07-05T10:53:05.516626+00:00"}