{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:F7V5AHX2NMCJ2OMDW6E67HC4ZY","short_pith_number":"pith:F7V5AHX2","schema_version":"1.0","canonical_sha256":"2febd01efa6b049d3983b789ef9c5cce3fd4172a8eac18ba6b270e91eceb9c81","source":{"kind":"arxiv","id":"2002.10937","version":2},"attestation_state":"computed","paper":{"title":"Diversity-Based Generalization for Unsupervised Text Classification under Domain Shift","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","stat.ML"],"primary_cat":"cs.LG","authors_text":"Hemant Purohit, Huzefa Rangwala, Jitin Krishnan","submitted_at":"2020-02-25T15:11:02Z","abstract_excerpt":"Domain adaptation approaches seek to learn from a source domain and generalize it to an unseen target domain. At present, the state-of-the-art unsupervised domain adaptation approaches for subjective text classification problems leverage unlabeled target data along with labeled source data. In this paper, we propose a novel method for domain adaptation of single-task text classification problems based on a simple but effective idea of diversity-based generalization that does not require unlabeled target data but still matches the state-of-the-art in performance. Diversity plays the role of 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":"2002.10937","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2020-02-25T15:11:02Z","cross_cats_sorted":["cs.CL","stat.ML"],"title_canon_sha256":"119acb6e8984210ea21a78d309242dce1c857f3e14b759a6864752ec1cf7f8bc","abstract_canon_sha256":"0f76708cb3fe2f12bb2fcf270fdd8e8873d977b479a0b57071715e3f0c80157a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:44:47.054134Z","signature_b64":"qWHmNPLiBF++iUi1VQB9b0FV1ehK6HDngRYpl4c9bFhGbnviSb7c55wTP5loLlB2tor4YpkRa5CWnGXUffYBCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2febd01efa6b049d3983b789ef9c5cce3fd4172a8eac18ba6b270e91eceb9c81","last_reissued_at":"2026-07-05T01:44:47.053723Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:44:47.053723Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Diversity-Based Generalization for Unsupervised Text Classification under Domain Shift","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","stat.ML"],"primary_cat":"cs.LG","authors_text":"Hemant Purohit, Huzefa Rangwala, Jitin Krishnan","submitted_at":"2020-02-25T15:11:02Z","abstract_excerpt":"Domain adaptation approaches seek to learn from a source domain and generalize it to an unseen target domain. At present, the state-of-the-art unsupervised domain adaptation approaches for subjective text classification problems leverage unlabeled target data along with labeled source data. In this paper, we propose a novel method for domain adaptation of single-task text classification problems based on a simple but effective idea of diversity-based generalization that does not require unlabeled target data but still matches the state-of-the-art in performance. Diversity plays the role of pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.10937","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/2002.10937/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":"2002.10937","created_at":"2026-07-05T01:44:47.053779+00:00"},{"alias_kind":"arxiv_version","alias_value":"2002.10937v2","created_at":"2026-07-05T01:44:47.053779+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.10937","created_at":"2026-07-05T01:44:47.053779+00:00"},{"alias_kind":"pith_short_12","alias_value":"F7V5AHX2NMCJ","created_at":"2026-07-05T01:44:47.053779+00:00"},{"alias_kind":"pith_short_16","alias_value":"F7V5AHX2NMCJ2OMD","created_at":"2026-07-05T01:44:47.053779+00:00"},{"alias_kind":"pith_short_8","alias_value":"F7V5AHX2","created_at":"2026-07-05T01:44:47.053779+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/F7V5AHX2NMCJ2OMDW6E67HC4ZY","json":"https://pith.science/pith/F7V5AHX2NMCJ2OMDW6E67HC4ZY.json","graph_json":"https://pith.science/api/pith-number/F7V5AHX2NMCJ2OMDW6E67HC4ZY/graph.json","events_json":"https://pith.science/api/pith-number/F7V5AHX2NMCJ2OMDW6E67HC4ZY/events.json","paper":"https://pith.science/paper/F7V5AHX2"},"agent_actions":{"view_html":"https://pith.science/pith/F7V5AHX2NMCJ2OMDW6E67HC4ZY","download_json":"https://pith.science/pith/F7V5AHX2NMCJ2OMDW6E67HC4ZY.json","view_paper":"https://pith.science/paper/F7V5AHX2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2002.10937&json=true","fetch_graph":"https://pith.science/api/pith-number/F7V5AHX2NMCJ2OMDW6E67HC4ZY/graph.json","fetch_events":"https://pith.science/api/pith-number/F7V5AHX2NMCJ2OMDW6E67HC4ZY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/F7V5AHX2NMCJ2OMDW6E67HC4ZY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/F7V5AHX2NMCJ2OMDW6E67HC4ZY/action/storage_attestation","attest_author":"https://pith.science/pith/F7V5AHX2NMCJ2OMDW6E67HC4ZY/action/author_attestation","sign_citation":"https://pith.science/pith/F7V5AHX2NMCJ2OMDW6E67HC4ZY/action/citation_signature","submit_replication":"https://pith.science/pith/F7V5AHX2NMCJ2OMDW6E67HC4ZY/action/replication_record"}},"created_at":"2026-07-05T01:44:47.053779+00:00","updated_at":"2026-07-05T01:44:47.053779+00:00"}