{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:RGPFNDVH2ZUNASJU3PDNWZ2RMO","short_pith_number":"pith:RGPFNDVH","schema_version":"1.0","canonical_sha256":"899e568ea7d668d04934dbc6db67516395df6110606ffadea4f47bd439b89d45","source":{"kind":"arxiv","id":"1907.03227","version":1},"attestation_state":"computed","paper":{"title":"Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Amir Pouran Ben Veyseh, Dejing Dou, Thien Huu Nguyen","submitted_at":"2019-07-07T06:02:05Z","abstract_excerpt":"Event factuality prediction (EFP) is the task of assessing the degree to which an event mentioned in a sentence has happened. For this task, both syntactic and semantic information are crucial to identify the important context words. The previous work for EFP has only combined these information in a simple way that cannot fully exploit their coordination. In this work, we introduce a novel graph-based neural network for EFP that can integrate the semantic and syntactic information more effectively. Our experiments demonstrate the advantage of the proposed model for EFP."},"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":"1907.03227","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-07-07T06:02:05Z","cross_cats_sorted":[],"title_canon_sha256":"61658083e6f45307f1d0c163bb3290dcf9ca48f3e212258d754e6855c15dfc7c","abstract_canon_sha256":"c09c1cda4c11f992142af4983f3be962536e9572ba0564ca81f581dcdc3dbeb7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:16.835112Z","signature_b64":"c3KW2b8kkaukWnKPv/tbq1zg3sD8ob72IapOp4aKSKqQDcsC21PyF5DeSeNd2u3VT3GhVSWyY8gCBptanvQXAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"899e568ea7d668d04934dbc6db67516395df6110606ffadea4f47bd439b89d45","last_reissued_at":"2026-05-17T23:41:16.834658Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:16.834658Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Amir Pouran Ben Veyseh, Dejing Dou, Thien Huu Nguyen","submitted_at":"2019-07-07T06:02:05Z","abstract_excerpt":"Event factuality prediction (EFP) is the task of assessing the degree to which an event mentioned in a sentence has happened. For this task, both syntactic and semantic information are crucial to identify the important context words. The previous work for EFP has only combined these information in a simple way that cannot fully exploit their coordination. In this work, we introduce a novel graph-based neural network for EFP that can integrate the semantic and syntactic information more effectively. Our experiments demonstrate the advantage of the proposed model for EFP."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.03227","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":""},"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":"1907.03227","created_at":"2026-05-17T23:41:16.834736+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.03227v1","created_at":"2026-05-17T23:41:16.834736+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.03227","created_at":"2026-05-17T23:41:16.834736+00:00"},{"alias_kind":"pith_short_12","alias_value":"RGPFNDVH2ZUN","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"RGPFNDVH2ZUNASJU","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"RGPFNDVH","created_at":"2026-05-18T12:33:27.125529+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/RGPFNDVH2ZUNASJU3PDNWZ2RMO","json":"https://pith.science/pith/RGPFNDVH2ZUNASJU3PDNWZ2RMO.json","graph_json":"https://pith.science/api/pith-number/RGPFNDVH2ZUNASJU3PDNWZ2RMO/graph.json","events_json":"https://pith.science/api/pith-number/RGPFNDVH2ZUNASJU3PDNWZ2RMO/events.json","paper":"https://pith.science/paper/RGPFNDVH"},"agent_actions":{"view_html":"https://pith.science/pith/RGPFNDVH2ZUNASJU3PDNWZ2RMO","download_json":"https://pith.science/pith/RGPFNDVH2ZUNASJU3PDNWZ2RMO.json","view_paper":"https://pith.science/paper/RGPFNDVH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.03227&json=true","fetch_graph":"https://pith.science/api/pith-number/RGPFNDVH2ZUNASJU3PDNWZ2RMO/graph.json","fetch_events":"https://pith.science/api/pith-number/RGPFNDVH2ZUNASJU3PDNWZ2RMO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RGPFNDVH2ZUNASJU3PDNWZ2RMO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RGPFNDVH2ZUNASJU3PDNWZ2RMO/action/storage_attestation","attest_author":"https://pith.science/pith/RGPFNDVH2ZUNASJU3PDNWZ2RMO/action/author_attestation","sign_citation":"https://pith.science/pith/RGPFNDVH2ZUNASJU3PDNWZ2RMO/action/citation_signature","submit_replication":"https://pith.science/pith/RGPFNDVH2ZUNASJU3PDNWZ2RMO/action/replication_record"}},"created_at":"2026-05-17T23:41:16.834736+00:00","updated_at":"2026-05-17T23:41:16.834736+00:00"}