{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:F5SKE7HZIJGFOPSN27BOLGSBVA","short_pith_number":"pith:F5SKE7HZ","canonical_record":{"source":{"id":"1806.04346","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-06-12T06:04:11Z","cross_cats_sorted":[],"title_canon_sha256":"e08a72339c011be87186b835c50facdca9f22fc7cb1f2837e971b0f904a3e6fb","abstract_canon_sha256":"935c473b8fdc30aaefc9e1ec4143b79d0c4b6a1e781cea65cd2aa6535cd11a2a"},"schema_version":"1.0"},"canonical_sha256":"2f64a27cf9424c573e4dd7c2e59a41a82968a83dc4319bab950a819d8471ead3","source":{"kind":"arxiv","id":"1806.04346","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.04346","created_at":"2026-05-18T00:13:35Z"},{"alias_kind":"arxiv_version","alias_value":"1806.04346v1","created_at":"2026-05-18T00:13:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.04346","created_at":"2026-05-18T00:13:35Z"},{"alias_kind":"pith_short_12","alias_value":"F5SKE7HZIJGF","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"F5SKE7HZIJGFOPSN","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"F5SKE7HZ","created_at":"2026-05-18T12:32:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:F5SKE7HZIJGFOPSN27BOLGSBVA","target":"record","payload":{"canonical_record":{"source":{"id":"1806.04346","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-06-12T06:04:11Z","cross_cats_sorted":[],"title_canon_sha256":"e08a72339c011be87186b835c50facdca9f22fc7cb1f2837e971b0f904a3e6fb","abstract_canon_sha256":"935c473b8fdc30aaefc9e1ec4143b79d0c4b6a1e781cea65cd2aa6535cd11a2a"},"schema_version":"1.0"},"canonical_sha256":"2f64a27cf9424c573e4dd7c2e59a41a82968a83dc4319bab950a819d8471ead3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:35.814419Z","signature_b64":"NmB3Lv5H44DnygfsOlJeaLWlsdq32t7Veyc/TNlgWuEk8QUpfIG7tcjh1/nipnTqA+fhlYFBis9wkuZKvAmxCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2f64a27cf9424c573e4dd7c2e59a41a82968a83dc4319bab950a819d8471ead3","last_reissued_at":"2026-05-18T00:13:35.813817Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:35.813817Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.04346","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:13:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pouZ5Gz5097MUWbhuMxjOPNqUztT+O1u0xK64M3ONR+w4BHUr3SFfN0YAj1VbGxQ77uBxIO1ESjr5uum68ObCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T07:26:32.805157Z"},"content_sha256":"140dc06acd54f20fa3dd659b7c6a53e2977326e43f75193ad32375c5c249ca31","schema_version":"1.0","event_id":"sha256:140dc06acd54f20fa3dd659b7c6a53e2977326e43f75193ad32375c5c249ca31"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:F5SKE7HZIJGFOPSN27BOLGSBVA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Exploiting Document Knowledge for Aspect-level Sentiment Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Daniel Dahlmeier, Hwee Tou Ng, Ruidan He, Wee Sun Lee","submitted_at":"2018-06-12T06:04:11Z","abstract_excerpt":"Attention-based long short-term memory (LSTM) networks have proven to be useful in aspect-level sentiment classification. However, due to the difficulties in annotating aspect-level data, existing public datasets for this task are all relatively small, which largely limits the effectiveness of those neural models. In this paper, we explore two approaches that transfer knowledge from document- level data, which is much less expensive to obtain, to improve the performance of aspect-level sentiment classification. We demonstrate the effectiveness of our approaches on 4 public datasets from SemEva"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.04346","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:13:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Lvld4A3BSJLhdslU7s0bWUy9ZSYoBvIJNEbdl1Y1rPOE7DW87uQV0tJFQ9NJS1Va2Gh+RWs3Y9wZ4nzq92sZBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T07:26:32.805512Z"},"content_sha256":"21b3c800243cce887658b89d2dd040a8639f087037e0d7c28f1c1e0dec37ea4d","schema_version":"1.0","event_id":"sha256:21b3c800243cce887658b89d2dd040a8639f087037e0d7c28f1c1e0dec37ea4d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F5SKE7HZIJGFOPSN27BOLGSBVA/bundle.json","state_url":"https://pith.science/pith/F5SKE7HZIJGFOPSN27BOLGSBVA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F5SKE7HZIJGFOPSN27BOLGSBVA/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-28T07:26:32Z","links":{"resolver":"https://pith.science/pith/F5SKE7HZIJGFOPSN27BOLGSBVA","bundle":"https://pith.science/pith/F5SKE7HZIJGFOPSN27BOLGSBVA/bundle.json","state":"https://pith.science/pith/F5SKE7HZIJGFOPSN27BOLGSBVA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F5SKE7HZIJGFOPSN27BOLGSBVA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:F5SKE7HZIJGFOPSN27BOLGSBVA","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"935c473b8fdc30aaefc9e1ec4143b79d0c4b6a1e781cea65cd2aa6535cd11a2a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-06-12T06:04:11Z","title_canon_sha256":"e08a72339c011be87186b835c50facdca9f22fc7cb1f2837e971b0f904a3e6fb"},"schema_version":"1.0","source":{"id":"1806.04346","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.04346","created_at":"2026-05-18T00:13:35Z"},{"alias_kind":"arxiv_version","alias_value":"1806.04346v1","created_at":"2026-05-18T00:13:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.04346","created_at":"2026-05-18T00:13:35Z"},{"alias_kind":"pith_short_12","alias_value":"F5SKE7HZIJGF","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"F5SKE7HZIJGFOPSN","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"F5SKE7HZ","created_at":"2026-05-18T12:32:22Z"}],"graph_snapshots":[{"event_id":"sha256:21b3c800243cce887658b89d2dd040a8639f087037e0d7c28f1c1e0dec37ea4d","target":"graph","created_at":"2026-05-18T00:13:35Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Attention-based long short-term memory (LSTM) networks have proven to be useful in aspect-level sentiment classification. However, due to the difficulties in annotating aspect-level data, existing public datasets for this task are all relatively small, which largely limits the effectiveness of those neural models. In this paper, we explore two approaches that transfer knowledge from document- level data, which is much less expensive to obtain, to improve the performance of aspect-level sentiment classification. We demonstrate the effectiveness of our approaches on 4 public datasets from SemEva","authors_text":"Daniel Dahlmeier, Hwee Tou Ng, Ruidan He, Wee Sun Lee","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-06-12T06:04:11Z","title":"Exploiting Document Knowledge for Aspect-level Sentiment Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.04346","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:140dc06acd54f20fa3dd659b7c6a53e2977326e43f75193ad32375c5c249ca31","target":"record","created_at":"2026-05-18T00:13:35Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"935c473b8fdc30aaefc9e1ec4143b79d0c4b6a1e781cea65cd2aa6535cd11a2a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-06-12T06:04:11Z","title_canon_sha256":"e08a72339c011be87186b835c50facdca9f22fc7cb1f2837e971b0f904a3e6fb"},"schema_version":"1.0","source":{"id":"1806.04346","kind":"arxiv","version":1}},"canonical_sha256":"2f64a27cf9424c573e4dd7c2e59a41a82968a83dc4319bab950a819d8471ead3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2f64a27cf9424c573e4dd7c2e59a41a82968a83dc4319bab950a819d8471ead3","first_computed_at":"2026-05-18T00:13:35.813817Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:13:35.813817Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NmB3Lv5H44DnygfsOlJeaLWlsdq32t7Veyc/TNlgWuEk8QUpfIG7tcjh1/nipnTqA+fhlYFBis9wkuZKvAmxCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:13:35.814419Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.04346","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:140dc06acd54f20fa3dd659b7c6a53e2977326e43f75193ad32375c5c249ca31","sha256:21b3c800243cce887658b89d2dd040a8639f087037e0d7c28f1c1e0dec37ea4d"],"state_sha256":"0774fc345a566587e2344d3ed8212c8a012c006ae3d3230c94ee432adda135a8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wrEGIgqYMGtjWdgoJsNuAw0Oeawu9d2PHa8UoJftslckJFZ1P6WE7+xD4Ik3mTBjXJ4jsFxIzsc8cj9G93Y5Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T07:26:32.807456Z","bundle_sha256":"276b21d85e4f15c45ae936f51c23beecbfb7b66e618a68c6c62d455a3dfa23b6"}}