{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:HNOOJMMHLCHCTZPK67KGZISABH","short_pith_number":"pith:HNOOJMMH","schema_version":"1.0","canonical_sha256":"3b5ce4b187588e29e5eaf7d46ca24009c9284e4e08fcdb02159583abf49793ec","source":{"kind":"arxiv","id":"2107.13764","version":1},"attestation_state":"computed","paper":{"title":"Term Expansion and FinBERT fine-tuning for Hypernym and Synonym Ranking of Financial Terms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ankush Chopra, Sohom Ghosh","submitted_at":"2021-07-29T06:17:44Z","abstract_excerpt":"Hypernym and synonym matching are one of the mainstream Natural Language Processing (NLP) tasks. In this paper, we present systems that attempt to solve this problem. We designed these systems to participate in the FinSim-3, a shared task of FinNLP workshop at IJCAI-2021. The shared task is focused on solving this problem for the financial domain. We experimented with various transformer based pre-trained embeddings by fine-tuning these for either classification or phrase similarity tasks. We also augmented the provided dataset with abbreviations derived from prospectus provided by the organiz"},"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":"2107.13764","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-07-29T06:17:44Z","cross_cats_sorted":[],"title_canon_sha256":"d8313a3674fb3f8caefd71d207b35910a6718ae57198989b4cdcb893cceedb96","abstract_canon_sha256":"2f265fe8830c93c455e6c3e99c7954b9d3edf98d9a82e97621ad98cdd959bbec"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:01:40.013529Z","signature_b64":"D3zBYbsE2VVZgAcFpunV/6fBv0yFGAuQ4zATqoz5XfTAFc0+gzyLtDXfbAoKwnEwQB42O8x42Zr77V3kylezAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3b5ce4b187588e29e5eaf7d46ca24009c9284e4e08fcdb02159583abf49793ec","last_reissued_at":"2026-07-05T03:01:40.013188Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:01:40.013188Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Term Expansion and FinBERT fine-tuning for Hypernym and Synonym Ranking of Financial Terms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ankush Chopra, Sohom Ghosh","submitted_at":"2021-07-29T06:17:44Z","abstract_excerpt":"Hypernym and synonym matching are one of the mainstream Natural Language Processing (NLP) tasks. In this paper, we present systems that attempt to solve this problem. We designed these systems to participate in the FinSim-3, a shared task of FinNLP workshop at IJCAI-2021. The shared task is focused on solving this problem for the financial domain. We experimented with various transformer based pre-trained embeddings by fine-tuning these for either classification or phrase similarity tasks. We also augmented the provided dataset with abbreviations derived from prospectus provided by the organiz"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.13764","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/2107.13764/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":"2107.13764","created_at":"2026-07-05T03:01:40.013243+00:00"},{"alias_kind":"arxiv_version","alias_value":"2107.13764v1","created_at":"2026-07-05T03:01:40.013243+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.13764","created_at":"2026-07-05T03:01:40.013243+00:00"},{"alias_kind":"pith_short_12","alias_value":"HNOOJMMHLCHC","created_at":"2026-07-05T03:01:40.013243+00:00"},{"alias_kind":"pith_short_16","alias_value":"HNOOJMMHLCHCTZPK","created_at":"2026-07-05T03:01:40.013243+00:00"},{"alias_kind":"pith_short_8","alias_value":"HNOOJMMH","created_at":"2026-07-05T03:01:40.013243+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/HNOOJMMHLCHCTZPK67KGZISABH","json":"https://pith.science/pith/HNOOJMMHLCHCTZPK67KGZISABH.json","graph_json":"https://pith.science/api/pith-number/HNOOJMMHLCHCTZPK67KGZISABH/graph.json","events_json":"https://pith.science/api/pith-number/HNOOJMMHLCHCTZPK67KGZISABH/events.json","paper":"https://pith.science/paper/HNOOJMMH"},"agent_actions":{"view_html":"https://pith.science/pith/HNOOJMMHLCHCTZPK67KGZISABH","download_json":"https://pith.science/pith/HNOOJMMHLCHCTZPK67KGZISABH.json","view_paper":"https://pith.science/paper/HNOOJMMH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2107.13764&json=true","fetch_graph":"https://pith.science/api/pith-number/HNOOJMMHLCHCTZPK67KGZISABH/graph.json","fetch_events":"https://pith.science/api/pith-number/HNOOJMMHLCHCTZPK67KGZISABH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HNOOJMMHLCHCTZPK67KGZISABH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HNOOJMMHLCHCTZPK67KGZISABH/action/storage_attestation","attest_author":"https://pith.science/pith/HNOOJMMHLCHCTZPK67KGZISABH/action/author_attestation","sign_citation":"https://pith.science/pith/HNOOJMMHLCHCTZPK67KGZISABH/action/citation_signature","submit_replication":"https://pith.science/pith/HNOOJMMHLCHCTZPK67KGZISABH/action/replication_record"}},"created_at":"2026-07-05T03:01:40.013243+00:00","updated_at":"2026-07-05T03:01:40.013243+00:00"}