{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:TIVTXCXKVWE3JQQLG5D5W6EUKK","short_pith_number":"pith:TIVTXCXK","schema_version":"1.0","canonical_sha256":"9a2b3b8aeaad89b4c20b3747db789452b27927c4183b26656f8c3d799fab5e0e","source":{"kind":"arxiv","id":"1802.00985","version":2},"attestation_state":"computed","paper":{"title":"Modeling Text with Graph Convolutional Network for Cross-Modal Information Retrieval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Jianlong Tan, Jing Yu, Li Guo, Weifeng Zhang, Yanbing Liu, Yuhang Lu, Zengchang Qin","submitted_at":"2018-02-03T15:05:01Z","abstract_excerpt":"Cross-modal information retrieval aims to find heterogeneous data of various modalities from a given query of one modality. The main challenge is to map different modalities into a common semantic space, in which distance between concepts in different modalities can be well modeled. For cross-modal information retrieval between images and texts, existing work mostly uses off-the-shelf Convolutional Neural Network (CNN) for image feature extraction. For texts, word-level features such as bag-of-words or word2vec are employed to build deep learning models to represent texts. Besides word-level 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":"1802.00985","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-02-03T15:05:01Z","cross_cats_sorted":[],"title_canon_sha256":"ccc29ac1fa14640a2bb1eab9983a5403a1b094aef3765f2eb04a80d4a4ec3463","abstract_canon_sha256":"008b6042a01eb4245446e9ca45bd7fc94f2f87d364c94ccdda4362de91743cf8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:23:44.754507Z","signature_b64":"6c9Qp/cbu552J7n6tU8UfWMdhPwhKH4FqE4sC9ltA0riORhD/ABSHBd6+cg0S6hUR+fyqXl72zWPAszZ1wGtDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9a2b3b8aeaad89b4c20b3747db789452b27927c4183b26656f8c3d799fab5e0e","last_reissued_at":"2026-05-18T00:23:44.753859Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:23:44.753859Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Modeling Text with Graph Convolutional Network for Cross-Modal Information Retrieval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Jianlong Tan, Jing Yu, Li Guo, Weifeng Zhang, Yanbing Liu, Yuhang Lu, Zengchang Qin","submitted_at":"2018-02-03T15:05:01Z","abstract_excerpt":"Cross-modal information retrieval aims to find heterogeneous data of various modalities from a given query of one modality. The main challenge is to map different modalities into a common semantic space, in which distance between concepts in different modalities can be well modeled. For cross-modal information retrieval between images and texts, existing work mostly uses off-the-shelf Convolutional Neural Network (CNN) for image feature extraction. For texts, word-level features such as bag-of-words or word2vec are employed to build deep learning models to represent texts. Besides word-level s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.00985","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":"1802.00985","created_at":"2026-05-18T00:23:44.753960+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.00985v2","created_at":"2026-05-18T00:23:44.753960+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.00985","created_at":"2026-05-18T00:23:44.753960+00:00"},{"alias_kind":"pith_short_12","alias_value":"TIVTXCXKVWE3","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_16","alias_value":"TIVTXCXKVWE3JQQL","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_8","alias_value":"TIVTXCXK","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/TIVTXCXKVWE3JQQLG5D5W6EUKK","json":"https://pith.science/pith/TIVTXCXKVWE3JQQLG5D5W6EUKK.json","graph_json":"https://pith.science/api/pith-number/TIVTXCXKVWE3JQQLG5D5W6EUKK/graph.json","events_json":"https://pith.science/api/pith-number/TIVTXCXKVWE3JQQLG5D5W6EUKK/events.json","paper":"https://pith.science/paper/TIVTXCXK"},"agent_actions":{"view_html":"https://pith.science/pith/TIVTXCXKVWE3JQQLG5D5W6EUKK","download_json":"https://pith.science/pith/TIVTXCXKVWE3JQQLG5D5W6EUKK.json","view_paper":"https://pith.science/paper/TIVTXCXK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.00985&json=true","fetch_graph":"https://pith.science/api/pith-number/TIVTXCXKVWE3JQQLG5D5W6EUKK/graph.json","fetch_events":"https://pith.science/api/pith-number/TIVTXCXKVWE3JQQLG5D5W6EUKK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TIVTXCXKVWE3JQQLG5D5W6EUKK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TIVTXCXKVWE3JQQLG5D5W6EUKK/action/storage_attestation","attest_author":"https://pith.science/pith/TIVTXCXKVWE3JQQLG5D5W6EUKK/action/author_attestation","sign_citation":"https://pith.science/pith/TIVTXCXKVWE3JQQLG5D5W6EUKK/action/citation_signature","submit_replication":"https://pith.science/pith/TIVTXCXKVWE3JQQLG5D5W6EUKK/action/replication_record"}},"created_at":"2026-05-18T00:23:44.753960+00:00","updated_at":"2026-05-18T00:23:44.753960+00:00"}