{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:TXI7TFIOBDD7VTHGPAYLHGVXFP","short_pith_number":"pith:TXI7TFIO","schema_version":"1.0","canonical_sha256":"9dd1f9950e08c7facce67830b39ab72be80d747b9b292a4b2455f861201cf1f3","source":{"kind":"arxiv","id":"1904.03111","version":2},"attestation_state":"computed","paper":{"title":"PoMo: Generating Entity-Specific Post-Modifiers in Context","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dheeru Dua, Jun Seok Kang, Kevin Gimpel, Niranjan Balasubramanian, Robert L. Logan IV, Sameer Singh, Yang Chen, Zewei Chu","submitted_at":"2019-04-05T15:10:09Z","abstract_excerpt":"We introduce entity post-modifier generation as an instance of a collaborative writing task. Given a sentence about a target entity, the task is to automatically generate a post-modifier phrase that provides contextually relevant information about the entity. For example, for the sentence, \"Barack Obama, _______, supported the #MeToo movement.\", the phrase \"a father of two girls\" is a contextually relevant post-modifier. To this end, we build PoMo, a post-modifier dataset created automatically from news articles reflecting a journalistic need for incorporating entity information that is releva"},"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":"1904.03111","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-04-05T15:10:09Z","cross_cats_sorted":[],"title_canon_sha256":"5b8155ea4b7e55981afc11fd30f547860409bdeec802a01542551939f1a5d1d0","abstract_canon_sha256":"859874ea11c8cbccb9416f77025aa312e2480db83e3ef3e962c02208522a218d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:59.594422Z","signature_b64":"ywRor8iXsHxCu9yKEyCAGjW1xywe4EvIaSjHx/0ONaugGcCrhY9fmhSzOtmso2rSHjLZfMALgvLFXyJerdOxBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9dd1f9950e08c7facce67830b39ab72be80d747b9b292a4b2455f861201cf1f3","last_reissued_at":"2026-05-17T23:48:59.593897Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:59.593897Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PoMo: Generating Entity-Specific Post-Modifiers in Context","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dheeru Dua, Jun Seok Kang, Kevin Gimpel, Niranjan Balasubramanian, Robert L. Logan IV, Sameer Singh, Yang Chen, Zewei Chu","submitted_at":"2019-04-05T15:10:09Z","abstract_excerpt":"We introduce entity post-modifier generation as an instance of a collaborative writing task. Given a sentence about a target entity, the task is to automatically generate a post-modifier phrase that provides contextually relevant information about the entity. For example, for the sentence, \"Barack Obama, _______, supported the #MeToo movement.\", the phrase \"a father of two girls\" is a contextually relevant post-modifier. To this end, we build PoMo, a post-modifier dataset created automatically from news articles reflecting a journalistic need for incorporating entity information that is releva"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.03111","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":"1904.03111","created_at":"2026-05-17T23:48:59.593985+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.03111v2","created_at":"2026-05-17T23:48:59.593985+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.03111","created_at":"2026-05-17T23:48:59.593985+00:00"},{"alias_kind":"pith_short_12","alias_value":"TXI7TFIOBDD7","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_16","alias_value":"TXI7TFIOBDD7VTHG","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_8","alias_value":"TXI7TFIO","created_at":"2026-05-18T12:33:30.264802+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/TXI7TFIOBDD7VTHGPAYLHGVXFP","json":"https://pith.science/pith/TXI7TFIOBDD7VTHGPAYLHGVXFP.json","graph_json":"https://pith.science/api/pith-number/TXI7TFIOBDD7VTHGPAYLHGVXFP/graph.json","events_json":"https://pith.science/api/pith-number/TXI7TFIOBDD7VTHGPAYLHGVXFP/events.json","paper":"https://pith.science/paper/TXI7TFIO"},"agent_actions":{"view_html":"https://pith.science/pith/TXI7TFIOBDD7VTHGPAYLHGVXFP","download_json":"https://pith.science/pith/TXI7TFIOBDD7VTHGPAYLHGVXFP.json","view_paper":"https://pith.science/paper/TXI7TFIO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.03111&json=true","fetch_graph":"https://pith.science/api/pith-number/TXI7TFIOBDD7VTHGPAYLHGVXFP/graph.json","fetch_events":"https://pith.science/api/pith-number/TXI7TFIOBDD7VTHGPAYLHGVXFP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TXI7TFIOBDD7VTHGPAYLHGVXFP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TXI7TFIOBDD7VTHGPAYLHGVXFP/action/storage_attestation","attest_author":"https://pith.science/pith/TXI7TFIOBDD7VTHGPAYLHGVXFP/action/author_attestation","sign_citation":"https://pith.science/pith/TXI7TFIOBDD7VTHGPAYLHGVXFP/action/citation_signature","submit_replication":"https://pith.science/pith/TXI7TFIOBDD7VTHGPAYLHGVXFP/action/replication_record"}},"created_at":"2026-05-17T23:48:59.593985+00:00","updated_at":"2026-05-17T23:48:59.593985+00:00"}