{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:BRN3TCWNHY5TIQ27AIPR54S3DW","short_pith_number":"pith:BRN3TCWN","schema_version":"1.0","canonical_sha256":"0c5bb98acd3e3b34435f021f1ef25b1db7e4973cbb80966227c173d179b3a226","source":{"kind":"arxiv","id":"1706.01069","version":1},"attestation_state":"computed","paper":{"title":"CRNN: A Joint Neural Network for Redundancy Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Eugene Ch'ng, Simon See, Uwe Aickelin, Xinyu Fu","submitted_at":"2017-06-04T13:12:45Z","abstract_excerpt":"This paper proposes a novel framework for detecting redundancy in supervised sentence categorisation. Unlike traditional singleton neural network, our model incorporates character-aware convolutional neural network (Char-CNN) with character-aware recurrent neural network (Char-RNN) to form a convolutional recurrent neural network (CRNN). Our model benefits from Char-CNN in that only salient features are selected and fed into the integrated Char-RNN. Char-RNN effectively learns long sequence semantics via sophisticated update mechanism. We compare our framework against the state-of-the-art text"},"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":"1706.01069","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-04T13:12:45Z","cross_cats_sorted":[],"title_canon_sha256":"204b900873a505ee7a868b11afb7317e47ca9b8fc6959479695b5f476bb43021","abstract_canon_sha256":"6367d39dcfa3d686528afa8582c86bddc11fbcd537cb7d7d3b192a6b191384db"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:43:04.628613Z","signature_b64":"beecZi5haBoLwR3Gmp80QNBU/p8cpscp6vllee/P3/cCKd4oGJo8Nvs+WLRyJl/gIKA+kzmzs4k1mnMTCDaVDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c5bb98acd3e3b34435f021f1ef25b1db7e4973cbb80966227c173d179b3a226","last_reissued_at":"2026-05-18T00:43:04.627635Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:43:04.627635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CRNN: A Joint Neural Network for Redundancy Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Eugene Ch'ng, Simon See, Uwe Aickelin, Xinyu Fu","submitted_at":"2017-06-04T13:12:45Z","abstract_excerpt":"This paper proposes a novel framework for detecting redundancy in supervised sentence categorisation. Unlike traditional singleton neural network, our model incorporates character-aware convolutional neural network (Char-CNN) with character-aware recurrent neural network (Char-RNN) to form a convolutional recurrent neural network (CRNN). Our model benefits from Char-CNN in that only salient features are selected and fed into the integrated Char-RNN. Char-RNN effectively learns long sequence semantics via sophisticated update mechanism. We compare our framework against the state-of-the-art text"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.01069","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":"1706.01069","created_at":"2026-05-18T00:43:04.627773+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.01069v1","created_at":"2026-05-18T00:43:04.627773+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.01069","created_at":"2026-05-18T00:43:04.627773+00:00"},{"alias_kind":"pith_short_12","alias_value":"BRN3TCWNHY5T","created_at":"2026-05-18T12:31:08.081275+00:00"},{"alias_kind":"pith_short_16","alias_value":"BRN3TCWNHY5TIQ27","created_at":"2026-05-18T12:31:08.081275+00:00"},{"alias_kind":"pith_short_8","alias_value":"BRN3TCWN","created_at":"2026-05-18T12:31:08.081275+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/BRN3TCWNHY5TIQ27AIPR54S3DW","json":"https://pith.science/pith/BRN3TCWNHY5TIQ27AIPR54S3DW.json","graph_json":"https://pith.science/api/pith-number/BRN3TCWNHY5TIQ27AIPR54S3DW/graph.json","events_json":"https://pith.science/api/pith-number/BRN3TCWNHY5TIQ27AIPR54S3DW/events.json","paper":"https://pith.science/paper/BRN3TCWN"},"agent_actions":{"view_html":"https://pith.science/pith/BRN3TCWNHY5TIQ27AIPR54S3DW","download_json":"https://pith.science/pith/BRN3TCWNHY5TIQ27AIPR54S3DW.json","view_paper":"https://pith.science/paper/BRN3TCWN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.01069&json=true","fetch_graph":"https://pith.science/api/pith-number/BRN3TCWNHY5TIQ27AIPR54S3DW/graph.json","fetch_events":"https://pith.science/api/pith-number/BRN3TCWNHY5TIQ27AIPR54S3DW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BRN3TCWNHY5TIQ27AIPR54S3DW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BRN3TCWNHY5TIQ27AIPR54S3DW/action/storage_attestation","attest_author":"https://pith.science/pith/BRN3TCWNHY5TIQ27AIPR54S3DW/action/author_attestation","sign_citation":"https://pith.science/pith/BRN3TCWNHY5TIQ27AIPR54S3DW/action/citation_signature","submit_replication":"https://pith.science/pith/BRN3TCWNHY5TIQ27AIPR54S3DW/action/replication_record"}},"created_at":"2026-05-18T00:43:04.627773+00:00","updated_at":"2026-05-18T00:43:04.627773+00:00"}