{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:C4QIZSXEYXYV7P7PVQVKU6QVMX","short_pith_number":"pith:C4QIZSXE","schema_version":"1.0","canonical_sha256":"17208ccae4c5f15fbfefac2aaa7a1565d35ce50242a6c99fd7bff9c3801682c9","source":{"kind":"arxiv","id":"2305.08146","version":1},"attestation_state":"computed","paper":{"title":"ParaLS: Lexical Substitution via Pretrained Paraphraser","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jipeng Qiang, Kang Liu, Yi Zhu, Yunhao Yuan, Yun Li","submitted_at":"2023-05-14T12:49:16Z","abstract_excerpt":"Lexical substitution (LS) aims at finding appropriate substitutes for a target word in a sentence. Recently, LS methods based on pretrained language models have made remarkable progress, generating potential substitutes for a target word through analysis of its contextual surroundings. However, these methods tend to overlook the preservation of the sentence's meaning when generating the substitutes. This study explores how to generate the substitute candidates from a paraphraser, as the generated paraphrases from a paraphraser contain variations in word choice and preserve the sentence's meani"},"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":"2305.08146","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CL","submitted_at":"2023-05-14T12:49:16Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"89699a7099b4068f83442c570f7d83405daf37e0e10b9bad6eabc3c79d2aa12e","abstract_canon_sha256":"38ebb17c43727414cd276dbd1e2e17b67b91696d33c964f42e05310d17605366"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:09:58.202827Z","signature_b64":"UgYexzG6ejJkTzy13nZ4a3Z+qRM976rdUK3iFk9wzkl9dQWgoiiqzN4/wUsRvNwtDpvrCunYCfIptLO76R+eAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"17208ccae4c5f15fbfefac2aaa7a1565d35ce50242a6c99fd7bff9c3801682c9","last_reissued_at":"2026-07-05T06:09:58.202389Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:09:58.202389Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ParaLS: Lexical Substitution via Pretrained Paraphraser","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jipeng Qiang, Kang Liu, Yi Zhu, Yunhao Yuan, Yun Li","submitted_at":"2023-05-14T12:49:16Z","abstract_excerpt":"Lexical substitution (LS) aims at finding appropriate substitutes for a target word in a sentence. Recently, LS methods based on pretrained language models have made remarkable progress, generating potential substitutes for a target word through analysis of its contextual surroundings. However, these methods tend to overlook the preservation of the sentence's meaning when generating the substitutes. This study explores how to generate the substitute candidates from a paraphraser, as the generated paraphrases from a paraphraser contain variations in word choice and preserve the sentence's meani"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.08146","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/2305.08146/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":"2305.08146","created_at":"2026-07-05T06:09:58.202445+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.08146v1","created_at":"2026-07-05T06:09:58.202445+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.08146","created_at":"2026-07-05T06:09:58.202445+00:00"},{"alias_kind":"pith_short_12","alias_value":"C4QIZSXEYXYV","created_at":"2026-07-05T06:09:58.202445+00:00"},{"alias_kind":"pith_short_16","alias_value":"C4QIZSXEYXYV7P7P","created_at":"2026-07-05T06:09:58.202445+00:00"},{"alias_kind":"pith_short_8","alias_value":"C4QIZSXE","created_at":"2026-07-05T06:09:58.202445+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.17200","citing_title":"Calibrating Model-Based Evaluation Metrics for Summarization","ref_index":20,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/C4QIZSXEYXYV7P7PVQVKU6QVMX","json":"https://pith.science/pith/C4QIZSXEYXYV7P7PVQVKU6QVMX.json","graph_json":"https://pith.science/api/pith-number/C4QIZSXEYXYV7P7PVQVKU6QVMX/graph.json","events_json":"https://pith.science/api/pith-number/C4QIZSXEYXYV7P7PVQVKU6QVMX/events.json","paper":"https://pith.science/paper/C4QIZSXE"},"agent_actions":{"view_html":"https://pith.science/pith/C4QIZSXEYXYV7P7PVQVKU6QVMX","download_json":"https://pith.science/pith/C4QIZSXEYXYV7P7PVQVKU6QVMX.json","view_paper":"https://pith.science/paper/C4QIZSXE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.08146&json=true","fetch_graph":"https://pith.science/api/pith-number/C4QIZSXEYXYV7P7PVQVKU6QVMX/graph.json","fetch_events":"https://pith.science/api/pith-number/C4QIZSXEYXYV7P7PVQVKU6QVMX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C4QIZSXEYXYV7P7PVQVKU6QVMX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C4QIZSXEYXYV7P7PVQVKU6QVMX/action/storage_attestation","attest_author":"https://pith.science/pith/C4QIZSXEYXYV7P7PVQVKU6QVMX/action/author_attestation","sign_citation":"https://pith.science/pith/C4QIZSXEYXYV7P7PVQVKU6QVMX/action/citation_signature","submit_replication":"https://pith.science/pith/C4QIZSXEYXYV7P7PVQVKU6QVMX/action/replication_record"}},"created_at":"2026-07-05T06:09:58.202445+00:00","updated_at":"2026-07-05T06:09:58.202445+00:00"}