{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:MDLCDDRSXFDEWI6SVWSBWF3WOO","short_pith_number":"pith:MDLCDDRS","schema_version":"1.0","canonical_sha256":"60d6218e32b9464b23d2ada41b1776739b25b69c677c444ecdc69d7cc1b2bdda","source":{"kind":"arxiv","id":"1906.07382","version":1},"attestation_state":"computed","paper":{"title":"Curriculum Learning Strategies for Hindi-English Codemixed Sentiment Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Anirudh Dahiya, Dipti Mishra Sharma, Manish Shrivastava, Neeraj Battan","submitted_at":"2019-06-18T05:14:17Z","abstract_excerpt":"Sentiment Analysis and other semantic tasks are commonly used for social media textual analysis to gauge public opinion and make sense from the noise on social media. The language used on social media not only commonly diverges from the formal language, but is compounded by codemixing between languages, especially in large multilingual societies like India.\n  Traditional methods for learning semantic NLP tasks have long relied on end to end task specific training, requiring expensive data creation process, even more so for deep learning methods. This challenge is even more severe for resource "},"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":"1906.07382","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-18T05:14:17Z","cross_cats_sorted":[],"title_canon_sha256":"371d7db1a2f723f555f58e90b7eb0cb6da3c8500e076c63725fc66ea1d445422","abstract_canon_sha256":"d91e8ec5cc6056a7a5d3a46a469d537b64c812da617217d52a31a24550b04eb5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:08.029619Z","signature_b64":"05M+jI5Gr4Mx1rFkL3Vq0h5IfzdJFDcycJIykbmQ+//s/9tawcYJiBwLy41qcPRR5yB0hNoMEtJDPM2g4ZiQCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"60d6218e32b9464b23d2ada41b1776739b25b69c677c444ecdc69d7cc1b2bdda","last_reissued_at":"2026-05-17T23:43:08.029046Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:08.029046Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Curriculum Learning Strategies for Hindi-English Codemixed Sentiment Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Anirudh Dahiya, Dipti Mishra Sharma, Manish Shrivastava, Neeraj Battan","submitted_at":"2019-06-18T05:14:17Z","abstract_excerpt":"Sentiment Analysis and other semantic tasks are commonly used for social media textual analysis to gauge public opinion and make sense from the noise on social media. The language used on social media not only commonly diverges from the formal language, but is compounded by codemixing between languages, especially in large multilingual societies like India.\n  Traditional methods for learning semantic NLP tasks have long relied on end to end task specific training, requiring expensive data creation process, even more so for deep learning methods. This challenge is even more severe for resource "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.07382","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":"1906.07382","created_at":"2026-05-17T23:43:08.029135+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.07382v1","created_at":"2026-05-17T23:43:08.029135+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.07382","created_at":"2026-05-17T23:43:08.029135+00:00"},{"alias_kind":"pith_short_12","alias_value":"MDLCDDRSXFDE","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"MDLCDDRSXFDEWI6S","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"MDLCDDRS","created_at":"2026-05-18T12:33:21.387695+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/MDLCDDRSXFDEWI6SVWSBWF3WOO","json":"https://pith.science/pith/MDLCDDRSXFDEWI6SVWSBWF3WOO.json","graph_json":"https://pith.science/api/pith-number/MDLCDDRSXFDEWI6SVWSBWF3WOO/graph.json","events_json":"https://pith.science/api/pith-number/MDLCDDRSXFDEWI6SVWSBWF3WOO/events.json","paper":"https://pith.science/paper/MDLCDDRS"},"agent_actions":{"view_html":"https://pith.science/pith/MDLCDDRSXFDEWI6SVWSBWF3WOO","download_json":"https://pith.science/pith/MDLCDDRSXFDEWI6SVWSBWF3WOO.json","view_paper":"https://pith.science/paper/MDLCDDRS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.07382&json=true","fetch_graph":"https://pith.science/api/pith-number/MDLCDDRSXFDEWI6SVWSBWF3WOO/graph.json","fetch_events":"https://pith.science/api/pith-number/MDLCDDRSXFDEWI6SVWSBWF3WOO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MDLCDDRSXFDEWI6SVWSBWF3WOO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MDLCDDRSXFDEWI6SVWSBWF3WOO/action/storage_attestation","attest_author":"https://pith.science/pith/MDLCDDRSXFDEWI6SVWSBWF3WOO/action/author_attestation","sign_citation":"https://pith.science/pith/MDLCDDRSXFDEWI6SVWSBWF3WOO/action/citation_signature","submit_replication":"https://pith.science/pith/MDLCDDRSXFDEWI6SVWSBWF3WOO/action/replication_record"}},"created_at":"2026-05-17T23:43:08.029135+00:00","updated_at":"2026-05-17T23:43:08.029135+00:00"}