{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:SFUDS5L6JEEESEDZHQ6ZZWPNDA","short_pith_number":"pith:SFUDS5L6","schema_version":"1.0","canonical_sha256":"916839757e49084910793c3d9cd9ed18165b83e1eae5d323bb930189252a2760","source":{"kind":"arxiv","id":"1811.05085","version":2},"attestation_state":"computed","paper":{"title":"Domain Agnostic Real-Valued Specificity Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Greg Durrett, Junyi Jessy Li, Wei-Jen Ko","submitted_at":"2018-11-13T03:16:29Z","abstract_excerpt":"Sentence specificity quantifies the level of detail in a sentence, characterizing the organization of information in discourse. While this information is useful for many downstream applications, specificity prediction systems predict very coarse labels (binary or ternary) and are trained on and tailored toward specific domains (e.g., news). The goal of this work is to generalize specificity prediction to domains where no labeled data is available and output more nuanced real-valued specificity ratings.\n  We present an unsupervised domain adaptation system for sentence specificity prediction, s"},"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":"1811.05085","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-11-13T03:16:29Z","cross_cats_sorted":[],"title_canon_sha256":"850ab56dcd412349117c5c378ba34a8b078d2221491bbe63138e4c60d3a4b1aa","abstract_canon_sha256":"87816be74414a625c30f8c491dafb01b720f2b5af8a63bd4fa605144c996c7b3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:38.991594Z","signature_b64":"tcAzygvB1Q51Vf6B6msJfWBFEJp4PhlCjOfWh6Uwum2zX1NbtWMp1fZ9IymQpD9xIYXHWxt0FmxSMo2RtvdwAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"916839757e49084910793c3d9cd9ed18165b83e1eae5d323bb930189252a2760","last_reissued_at":"2026-05-18T00:00:38.991136Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:38.991136Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Domain Agnostic Real-Valued Specificity Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Greg Durrett, Junyi Jessy Li, Wei-Jen Ko","submitted_at":"2018-11-13T03:16:29Z","abstract_excerpt":"Sentence specificity quantifies the level of detail in a sentence, characterizing the organization of information in discourse. While this information is useful for many downstream applications, specificity prediction systems predict very coarse labels (binary or ternary) and are trained on and tailored toward specific domains (e.g., news). The goal of this work is to generalize specificity prediction to domains where no labeled data is available and output more nuanced real-valued specificity ratings.\n  We present an unsupervised domain adaptation system for sentence specificity prediction, s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.05085","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":""},"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":"1811.05085","created_at":"2026-05-18T00:00:38.991210+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.05085v2","created_at":"2026-05-18T00:00:38.991210+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.05085","created_at":"2026-05-18T00:00:38.991210+00:00"},{"alias_kind":"pith_short_12","alias_value":"SFUDS5L6JEEE","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_16","alias_value":"SFUDS5L6JEEESEDZ","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_8","alias_value":"SFUDS5L6","created_at":"2026-05-18T12:32:53.628368+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/SFUDS5L6JEEESEDZHQ6ZZWPNDA","json":"https://pith.science/pith/SFUDS5L6JEEESEDZHQ6ZZWPNDA.json","graph_json":"https://pith.science/api/pith-number/SFUDS5L6JEEESEDZHQ6ZZWPNDA/graph.json","events_json":"https://pith.science/api/pith-number/SFUDS5L6JEEESEDZHQ6ZZWPNDA/events.json","paper":"https://pith.science/paper/SFUDS5L6"},"agent_actions":{"view_html":"https://pith.science/pith/SFUDS5L6JEEESEDZHQ6ZZWPNDA","download_json":"https://pith.science/pith/SFUDS5L6JEEESEDZHQ6ZZWPNDA.json","view_paper":"https://pith.science/paper/SFUDS5L6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.05085&json=true","fetch_graph":"https://pith.science/api/pith-number/SFUDS5L6JEEESEDZHQ6ZZWPNDA/graph.json","fetch_events":"https://pith.science/api/pith-number/SFUDS5L6JEEESEDZHQ6ZZWPNDA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SFUDS5L6JEEESEDZHQ6ZZWPNDA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SFUDS5L6JEEESEDZHQ6ZZWPNDA/action/storage_attestation","attest_author":"https://pith.science/pith/SFUDS5L6JEEESEDZHQ6ZZWPNDA/action/author_attestation","sign_citation":"https://pith.science/pith/SFUDS5L6JEEESEDZHQ6ZZWPNDA/action/citation_signature","submit_replication":"https://pith.science/pith/SFUDS5L6JEEESEDZHQ6ZZWPNDA/action/replication_record"}},"created_at":"2026-05-18T00:00:38.991210+00:00","updated_at":"2026-05-18T00:00:38.991210+00:00"}