{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:ANA7TNCGHNEO4DBHAKVISC2H2S","short_pith_number":"pith:ANA7TNCG","schema_version":"1.0","canonical_sha256":"0341f9b4463b48ee0c2702aa890b47d4947678ce74318a05266c8190ade4e716","source":{"kind":"arxiv","id":"1805.01086","version":1},"attestation_state":"computed","paper":{"title":"Transformation Networks for Target-Oriented Sentiment Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bei Shi, Lidong Bing, Wai Lam, Xin Li","submitted_at":"2018-05-03T02:16:27Z","abstract_excerpt":"Target-oriented sentiment classification aims at classifying sentiment polarities over individual opinion targets in a sentence. RNN with attention seems a good fit for the characteristics of this task, and indeed it achieves the state-of-the-art performance. After re-examining the drawbacks of attention mechanism and the obstacles that block CNN to perform well in this classification task, we propose a new model to overcome these issues. Instead of attention, our model employs a CNN layer to extract salient features from the transformed word representations originated from a bi-directional RN"},"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":"1805.01086","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-05-03T02:16:27Z","cross_cats_sorted":[],"title_canon_sha256":"02ad09eeda223e7f627230f0a3eba7774d8da9ecd4611d46faa04dca05db6e21","abstract_canon_sha256":"5322827a5684360b317cf9551d576004f0a6797a54d4698cd065e93b72593f6b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:16:52.613721Z","signature_b64":"LcDuslI/erWyD2n0o2Qto2beFiMLlcNERzJY7fV7uxb7b+qEOPTshcXvwfjEtGKYSd0PG6n0hgPaCpcnciB3Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0341f9b4463b48ee0c2702aa890b47d4947678ce74318a05266c8190ade4e716","last_reissued_at":"2026-05-18T00:16:52.613180Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:16:52.613180Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Transformation Networks for Target-Oriented Sentiment Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bei Shi, Lidong Bing, Wai Lam, Xin Li","submitted_at":"2018-05-03T02:16:27Z","abstract_excerpt":"Target-oriented sentiment classification aims at classifying sentiment polarities over individual opinion targets in a sentence. RNN with attention seems a good fit for the characteristics of this task, and indeed it achieves the state-of-the-art performance. After re-examining the drawbacks of attention mechanism and the obstacles that block CNN to perform well in this classification task, we propose a new model to overcome these issues. Instead of attention, our model employs a CNN layer to extract salient features from the transformed word representations originated from a bi-directional RN"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.01086","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":"1805.01086","created_at":"2026-05-18T00:16:52.613273+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.01086v1","created_at":"2026-05-18T00:16:52.613273+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.01086","created_at":"2026-05-18T00:16:52.613273+00:00"},{"alias_kind":"pith_short_12","alias_value":"ANA7TNCGHNEO","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_16","alias_value":"ANA7TNCGHNEO4DBH","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_8","alias_value":"ANA7TNCG","created_at":"2026-05-18T12:32:13.499390+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/ANA7TNCGHNEO4DBHAKVISC2H2S","json":"https://pith.science/pith/ANA7TNCGHNEO4DBHAKVISC2H2S.json","graph_json":"https://pith.science/api/pith-number/ANA7TNCGHNEO4DBHAKVISC2H2S/graph.json","events_json":"https://pith.science/api/pith-number/ANA7TNCGHNEO4DBHAKVISC2H2S/events.json","paper":"https://pith.science/paper/ANA7TNCG"},"agent_actions":{"view_html":"https://pith.science/pith/ANA7TNCGHNEO4DBHAKVISC2H2S","download_json":"https://pith.science/pith/ANA7TNCGHNEO4DBHAKVISC2H2S.json","view_paper":"https://pith.science/paper/ANA7TNCG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.01086&json=true","fetch_graph":"https://pith.science/api/pith-number/ANA7TNCGHNEO4DBHAKVISC2H2S/graph.json","fetch_events":"https://pith.science/api/pith-number/ANA7TNCGHNEO4DBHAKVISC2H2S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ANA7TNCGHNEO4DBHAKVISC2H2S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ANA7TNCGHNEO4DBHAKVISC2H2S/action/storage_attestation","attest_author":"https://pith.science/pith/ANA7TNCGHNEO4DBHAKVISC2H2S/action/author_attestation","sign_citation":"https://pith.science/pith/ANA7TNCGHNEO4DBHAKVISC2H2S/action/citation_signature","submit_replication":"https://pith.science/pith/ANA7TNCGHNEO4DBHAKVISC2H2S/action/replication_record"}},"created_at":"2026-05-18T00:16:52.613273+00:00","updated_at":"2026-05-18T00:16:52.613273+00:00"}