{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:GVVXZH7T4KPCUTR3IOEEYNVURL","short_pith_number":"pith:GVVXZH7T","schema_version":"1.0","canonical_sha256":"356b7c9ff3e29e2a4e3b43884c36b48ae7b53b9c8e61f5e855dd713676b78695","source":{"kind":"arxiv","id":"2110.12200","version":2},"attestation_state":"computed","paper":{"title":"Hate and Offensive Speech Detection in Hindi and Marathi","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Abhishek Velankar, Amol Gore, Hrushikesh Patil, Raviraj Joshi, Shubham Salunke","submitted_at":"2021-10-23T11:57:36Z","abstract_excerpt":"Sentiment analysis is the most basic NLP task to determine the polarity of text data. There has been a significant amount of work in the area of multilingual text as well. Still hate and offensive speech detection faces a challenge due to inadequate availability of data, especially for Indian languages like Hindi and Marathi. In this work, we consider hate and offensive speech detection in Hindi and Marathi texts. The problem is formulated as a text classification task using the state of the art deep learning approaches. We explore different deep learning architectures like CNN, LSTM, and vari"},"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":"2110.12200","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-10-23T11:57:36Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2785a491938ec1a526d4c95a8c2271994b74c56e7d5efa4fe282437b2ba4d2bd","abstract_canon_sha256":"2170ea63502ec5fad7d51f9da985034c9f0125f8d84b21d54cb80a961df07f0d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:27:36.759727Z","signature_b64":"cAbf9uZyG/8+0+twYlUUCuE2815+6g6+uSINNmy7aZRZJwL+f2wI9mpalcSCGggrudSJlcYiFvpt3zH/8GvXCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"356b7c9ff3e29e2a4e3b43884c36b48ae7b53b9c8e61f5e855dd713676b78695","last_reissued_at":"2026-07-05T03:27:36.757832Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:27:36.757832Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hate and Offensive Speech Detection in Hindi and Marathi","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Abhishek Velankar, Amol Gore, Hrushikesh Patil, Raviraj Joshi, Shubham Salunke","submitted_at":"2021-10-23T11:57:36Z","abstract_excerpt":"Sentiment analysis is the most basic NLP task to determine the polarity of text data. There has been a significant amount of work in the area of multilingual text as well. Still hate and offensive speech detection faces a challenge due to inadequate availability of data, especially for Indian languages like Hindi and Marathi. In this work, we consider hate and offensive speech detection in Hindi and Marathi texts. The problem is formulated as a text classification task using the state of the art deep learning approaches. We explore different deep learning architectures like CNN, LSTM, and vari"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.12200","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/2110.12200/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":"2110.12200","created_at":"2026-07-05T03:27:36.757900+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.12200v2","created_at":"2026-07-05T03:27:36.757900+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.12200","created_at":"2026-07-05T03:27:36.757900+00:00"},{"alias_kind":"pith_short_12","alias_value":"GVVXZH7T4KPC","created_at":"2026-07-05T03:27:36.757900+00:00"},{"alias_kind":"pith_short_16","alias_value":"GVVXZH7T4KPCUTR3","created_at":"2026-07-05T03:27:36.757900+00:00"},{"alias_kind":"pith_short_8","alias_value":"GVVXZH7T","created_at":"2026-07-05T03:27:36.757900+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.18423","citing_title":"BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources","ref_index":58,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21370","citing_title":"MKJ at SemEval-2026 Task 9: A Comparative Study of Generalist, Specialist, and Ensemble Strategies for Multilingual Polarization","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GVVXZH7T4KPCUTR3IOEEYNVURL","json":"https://pith.science/pith/GVVXZH7T4KPCUTR3IOEEYNVURL.json","graph_json":"https://pith.science/api/pith-number/GVVXZH7T4KPCUTR3IOEEYNVURL/graph.json","events_json":"https://pith.science/api/pith-number/GVVXZH7T4KPCUTR3IOEEYNVURL/events.json","paper":"https://pith.science/paper/GVVXZH7T"},"agent_actions":{"view_html":"https://pith.science/pith/GVVXZH7T4KPCUTR3IOEEYNVURL","download_json":"https://pith.science/pith/GVVXZH7T4KPCUTR3IOEEYNVURL.json","view_paper":"https://pith.science/paper/GVVXZH7T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.12200&json=true","fetch_graph":"https://pith.science/api/pith-number/GVVXZH7T4KPCUTR3IOEEYNVURL/graph.json","fetch_events":"https://pith.science/api/pith-number/GVVXZH7T4KPCUTR3IOEEYNVURL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GVVXZH7T4KPCUTR3IOEEYNVURL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GVVXZH7T4KPCUTR3IOEEYNVURL/action/storage_attestation","attest_author":"https://pith.science/pith/GVVXZH7T4KPCUTR3IOEEYNVURL/action/author_attestation","sign_citation":"https://pith.science/pith/GVVXZH7T4KPCUTR3IOEEYNVURL/action/citation_signature","submit_replication":"https://pith.science/pith/GVVXZH7T4KPCUTR3IOEEYNVURL/action/replication_record"}},"created_at":"2026-07-05T03:27:36.757900+00:00","updated_at":"2026-07-05T03:27:36.757900+00:00"}