{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:46AONZLFAUUGDG43HXHUBXJ5XG","short_pith_number":"pith:46AONZLF","schema_version":"1.0","canonical_sha256":"e780e6e5650528619b9b3dcf40dd3db9b14566f4e7f5cbfa2054c7061a65d378","source":{"kind":"arxiv","id":"1906.01236","version":1},"attestation_state":"computed","paper":{"title":"RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Mengran Zhang, Rui Xia, Zixiang Ding","submitted_at":"2019-06-04T07:10:16Z","abstract_excerpt":"The emotion cause extraction (ECE) task aims at discovering the potential causes behind a certain emotion expression in a document. Techniques including rule-based methods, traditional machine learning methods and deep neural networks have been proposed to solve this task. However, most of the previous work considered ECE as a set of independent clause classification problems and ignored the relations between multiple clauses in a document. In this work, we propose a joint emotion cause extraction framework, named RNN-Transformer Hierarchical Network (RTHN), to encode and classify multiple cla"},"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":"1906.01236","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-04T07:10:16Z","cross_cats_sorted":[],"title_canon_sha256":"300024de603f9d76dafd0f5280c36b26263d75912f467cf51911edf8c8df3c53","abstract_canon_sha256":"a4af08bfe6e12b442ef6d2c7728219bafebc8d6989082b3a85414fc29b162791"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:17.423126Z","signature_b64":"OH5ldTAoIQFJfUiCN3s+YGiB7QLanj4MfLhddd15j4EPyIc1meuR90yOJ2GfP9M7pTXRAAeT91soN3BwgFc0DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e780e6e5650528619b9b3dcf40dd3db9b14566f4e7f5cbfa2054c7061a65d378","last_reissued_at":"2026-05-17T23:44:17.422378Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:17.422378Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Mengran Zhang, Rui Xia, Zixiang Ding","submitted_at":"2019-06-04T07:10:16Z","abstract_excerpt":"The emotion cause extraction (ECE) task aims at discovering the potential causes behind a certain emotion expression in a document. Techniques including rule-based methods, traditional machine learning methods and deep neural networks have been proposed to solve this task. However, most of the previous work considered ECE as a set of independent clause classification problems and ignored the relations between multiple clauses in a document. In this work, we propose a joint emotion cause extraction framework, named RNN-Transformer Hierarchical Network (RTHN), to encode and classify multiple cla"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.01236","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":"1906.01236","created_at":"2026-05-17T23:44:17.422502+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.01236v1","created_at":"2026-05-17T23:44:17.422502+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.01236","created_at":"2026-05-17T23:44:17.422502+00:00"},{"alias_kind":"pith_short_12","alias_value":"46AONZLFAUUG","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"46AONZLFAUUGDG43","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"46AONZLF","created_at":"2026-05-18T12:33:10.108867+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/46AONZLFAUUGDG43HXHUBXJ5XG","json":"https://pith.science/pith/46AONZLFAUUGDG43HXHUBXJ5XG.json","graph_json":"https://pith.science/api/pith-number/46AONZLFAUUGDG43HXHUBXJ5XG/graph.json","events_json":"https://pith.science/api/pith-number/46AONZLFAUUGDG43HXHUBXJ5XG/events.json","paper":"https://pith.science/paper/46AONZLF"},"agent_actions":{"view_html":"https://pith.science/pith/46AONZLFAUUGDG43HXHUBXJ5XG","download_json":"https://pith.science/pith/46AONZLFAUUGDG43HXHUBXJ5XG.json","view_paper":"https://pith.science/paper/46AONZLF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.01236&json=true","fetch_graph":"https://pith.science/api/pith-number/46AONZLFAUUGDG43HXHUBXJ5XG/graph.json","fetch_events":"https://pith.science/api/pith-number/46AONZLFAUUGDG43HXHUBXJ5XG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/46AONZLFAUUGDG43HXHUBXJ5XG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/46AONZLFAUUGDG43HXHUBXJ5XG/action/storage_attestation","attest_author":"https://pith.science/pith/46AONZLFAUUGDG43HXHUBXJ5XG/action/author_attestation","sign_citation":"https://pith.science/pith/46AONZLFAUUGDG43HXHUBXJ5XG/action/citation_signature","submit_replication":"https://pith.science/pith/46AONZLFAUUGDG43HXHUBXJ5XG/action/replication_record"}},"created_at":"2026-05-17T23:44:17.422502+00:00","updated_at":"2026-05-17T23:44:17.422502+00:00"}