{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5TKEWVEOLLWA6EMOEDN6OEBS2D","short_pith_number":"pith:5TKEWVEO","schema_version":"1.0","canonical_sha256":"ecd44b548e5aec0f118e20dbe71032d0fa16a037bec56daa9dd096c313cc4af9","source":{"kind":"arxiv","id":"2412.15375","version":1},"attestation_state":"computed","paper":{"title":"Automatic Extraction of Metaphoric Analogies from Literary Texts: Task Formulation, Dataset Construction, and Evaluation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dimosthenis Antypas, Hsuvas Borkakoty, Joanne Boisson, Jose Camacho-Collados, Luis Espinosa Anke, Zara Siddique","submitted_at":"2024-12-19T20:11:04Z","abstract_excerpt":"Extracting metaphors and analogies from free text requires high-level reasoning abilities such as abstraction and language understanding. Our study focuses on the extraction of the concepts that form metaphoric analogies in literary texts. To this end, we construct a novel dataset in this domain with the help of domain experts. We compare the out-of-the-box ability of recent large language models (LLMs) to structure metaphoric mappings from fragments of texts containing proportional analogies. The models are further evaluated on the generation of implicit elements of the analogy, which are ind"},"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":"2412.15375","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-19T20:11:04Z","cross_cats_sorted":[],"title_canon_sha256":"d7aca33845d6ad2b6c3d6323489fdf2bf12fdfe7594bed073d58674cd6fc68b8","abstract_canon_sha256":"943c9fd1352cab29b2dc4b6412ffbb33b9fd8f296021613f6cc6eb599a6aab6c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:52:22.204273Z","signature_b64":"PNDOC7nVJ4/NN+bkdFOlzbSfeNmepSYGglIBt2CjjZF/0L2bb6kR+A6QZiCaVFjQzqyo60pUydptdXr4G8d0BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ecd44b548e5aec0f118e20dbe71032d0fa16a037bec56daa9dd096c313cc4af9","last_reissued_at":"2026-07-05T09:52:22.203645Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:52:22.203645Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automatic Extraction of Metaphoric Analogies from Literary Texts: Task Formulation, Dataset Construction, and Evaluation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dimosthenis Antypas, Hsuvas Borkakoty, Joanne Boisson, Jose Camacho-Collados, Luis Espinosa Anke, Zara Siddique","submitted_at":"2024-12-19T20:11:04Z","abstract_excerpt":"Extracting metaphors and analogies from free text requires high-level reasoning abilities such as abstraction and language understanding. Our study focuses on the extraction of the concepts that form metaphoric analogies in literary texts. To this end, we construct a novel dataset in this domain with the help of domain experts. We compare the out-of-the-box ability of recent large language models (LLMs) to structure metaphoric mappings from fragments of texts containing proportional analogies. The models are further evaluated on the generation of implicit elements of the analogy, which are ind"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.15375","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/2412.15375/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":"2412.15375","created_at":"2026-07-05T09:52:22.203704+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.15375v1","created_at":"2026-07-05T09:52:22.203704+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.15375","created_at":"2026-07-05T09:52:22.203704+00:00"},{"alias_kind":"pith_short_12","alias_value":"5TKEWVEOLLWA","created_at":"2026-07-05T09:52:22.203704+00:00"},{"alias_kind":"pith_short_16","alias_value":"5TKEWVEOLLWA6EMO","created_at":"2026-07-05T09:52:22.203704+00:00"},{"alias_kind":"pith_short_8","alias_value":"5TKEWVEO","created_at":"2026-07-05T09:52:22.203704+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/5TKEWVEOLLWA6EMOEDN6OEBS2D","json":"https://pith.science/pith/5TKEWVEOLLWA6EMOEDN6OEBS2D.json","graph_json":"https://pith.science/api/pith-number/5TKEWVEOLLWA6EMOEDN6OEBS2D/graph.json","events_json":"https://pith.science/api/pith-number/5TKEWVEOLLWA6EMOEDN6OEBS2D/events.json","paper":"https://pith.science/paper/5TKEWVEO"},"agent_actions":{"view_html":"https://pith.science/pith/5TKEWVEOLLWA6EMOEDN6OEBS2D","download_json":"https://pith.science/pith/5TKEWVEOLLWA6EMOEDN6OEBS2D.json","view_paper":"https://pith.science/paper/5TKEWVEO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.15375&json=true","fetch_graph":"https://pith.science/api/pith-number/5TKEWVEOLLWA6EMOEDN6OEBS2D/graph.json","fetch_events":"https://pith.science/api/pith-number/5TKEWVEOLLWA6EMOEDN6OEBS2D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5TKEWVEOLLWA6EMOEDN6OEBS2D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5TKEWVEOLLWA6EMOEDN6OEBS2D/action/storage_attestation","attest_author":"https://pith.science/pith/5TKEWVEOLLWA6EMOEDN6OEBS2D/action/author_attestation","sign_citation":"https://pith.science/pith/5TKEWVEOLLWA6EMOEDN6OEBS2D/action/citation_signature","submit_replication":"https://pith.science/pith/5TKEWVEOLLWA6EMOEDN6OEBS2D/action/replication_record"}},"created_at":"2026-07-05T09:52:22.203704+00:00","updated_at":"2026-07-05T09:52:22.203704+00:00"}