{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:CQKHUFR3HMEBJ65KTMOKQ2YGTE","short_pith_number":"pith:CQKHUFR3","canonical_record":{"source":{"id":"1812.07860","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-12-19T10:21:20Z","cross_cats_sorted":[],"title_canon_sha256":"0ec9a6e98a39c2c17a354d9513c5d0784c42fdb1de90e749db6b0ab84ee0bf32","abstract_canon_sha256":"5ce0aedde12410bd8db9e492cfe706dc30f0f541970134ba5f4645c1afd66216"},"schema_version":"1.0"},"canonical_sha256":"14147a163b3b0814fbaa9b1ca86b06993ff92b6db2ddc46e1b0f0473be650287","source":{"kind":"arxiv","id":"1812.07860","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.07860","created_at":"2026-05-17T23:57:55Z"},{"alias_kind":"arxiv_version","alias_value":"1812.07860v1","created_at":"2026-05-17T23:57:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.07860","created_at":"2026-05-17T23:57:55Z"},{"alias_kind":"pith_short_12","alias_value":"CQKHUFR3HMEB","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"CQKHUFR3HMEBJ65K","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"CQKHUFR3","created_at":"2026-05-18T12:32:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:CQKHUFR3HMEBJ65KTMOKQ2YGTE","target":"record","payload":{"canonical_record":{"source":{"id":"1812.07860","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-12-19T10:21:20Z","cross_cats_sorted":[],"title_canon_sha256":"0ec9a6e98a39c2c17a354d9513c5d0784c42fdb1de90e749db6b0ab84ee0bf32","abstract_canon_sha256":"5ce0aedde12410bd8db9e492cfe706dc30f0f541970134ba5f4645c1afd66216"},"schema_version":"1.0"},"canonical_sha256":"14147a163b3b0814fbaa9b1ca86b06993ff92b6db2ddc46e1b0f0473be650287","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:55.716698Z","signature_b64":"/cUv67qWCyq1mBSblHWNxwtQOipSzNzX+TNWAHr/+lXrhkdSAWh8TW1TURe6c9GnRos4uoFck+okHtjwWgMMBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"14147a163b3b0814fbaa9b1ca86b06993ff92b6db2ddc46e1b0f0473be650287","last_reissued_at":"2026-05-17T23:57:55.716108Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:55.716108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.07860","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":"2026-05-17T23:57:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I92G5++RphdhbEDmAo5RH3lDBK67ySl/r+jf737RrdXSM+cfZTaE4XCIpozvEWTI5igjjV8o/fOXTewE9YJcCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T12:26:53.211600Z"},"content_sha256":"ef0f9c50e8fb44c1b9be872c5aaf1d20494c045564700f5cbb3f26413c964f8c","schema_version":"1.0","event_id":"sha256:ef0f9c50e8fb44c1b9be872c5aaf1d20494c045564700f5cbb3f26413c964f8c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:CQKHUFR3HMEBJ65KTMOKQ2YGTE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Self-Attention: A Better Building Block for Sentiment Analysis Neural Network Classifiers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Artaches Ambartsoumian, Fred Popowich","submitted_at":"2018-12-19T10:21:20Z","abstract_excerpt":"Sentiment Analysis has seen much progress in the past two decades. For the past few years, neural network approaches, primarily RNNs and CNNs, have been the most successful for this task. Recently, a new category of neural networks, self-attention networks (SANs), have been created which utilizes the attention mechanism as the basic building block. Self-attention networks have been shown to be effective for sequence modeling tasks, while having no recurrence or convolutions. In this work we explore the effectiveness of the SANs for sentiment analysis. We demonstrate that SANs are superior in p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.07860","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":"2026-05-17T23:57:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0THq+2kA82mP4kRexKn7cR9wrooElrAWL5ZuxWAg5G6zhfGqE7CbCp9lPz0RtvB/WuWFqHNMm6CZA90OuI7XDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T12:26:53.212336Z"},"content_sha256":"3a12f64b71fc3191ca133492538eb49123a09ab8609753b853aca6e965e7f3da","schema_version":"1.0","event_id":"sha256:3a12f64b71fc3191ca133492538eb49123a09ab8609753b853aca6e965e7f3da"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CQKHUFR3HMEBJ65KTMOKQ2YGTE/bundle.json","state_url":"https://pith.science/pith/CQKHUFR3HMEBJ65KTMOKQ2YGTE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CQKHUFR3HMEBJ65KTMOKQ2YGTE/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":"2026-05-23T12:26:53Z","links":{"resolver":"https://pith.science/pith/CQKHUFR3HMEBJ65KTMOKQ2YGTE","bundle":"https://pith.science/pith/CQKHUFR3HMEBJ65KTMOKQ2YGTE/bundle.json","state":"https://pith.science/pith/CQKHUFR3HMEBJ65KTMOKQ2YGTE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CQKHUFR3HMEBJ65KTMOKQ2YGTE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:CQKHUFR3HMEBJ65KTMOKQ2YGTE","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":"5ce0aedde12410bd8db9e492cfe706dc30f0f541970134ba5f4645c1afd66216","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-12-19T10:21:20Z","title_canon_sha256":"0ec9a6e98a39c2c17a354d9513c5d0784c42fdb1de90e749db6b0ab84ee0bf32"},"schema_version":"1.0","source":{"id":"1812.07860","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.07860","created_at":"2026-05-17T23:57:55Z"},{"alias_kind":"arxiv_version","alias_value":"1812.07860v1","created_at":"2026-05-17T23:57:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.07860","created_at":"2026-05-17T23:57:55Z"},{"alias_kind":"pith_short_12","alias_value":"CQKHUFR3HMEB","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"CQKHUFR3HMEBJ65K","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"CQKHUFR3","created_at":"2026-05-18T12:32:16Z"}],"graph_snapshots":[{"event_id":"sha256:3a12f64b71fc3191ca133492538eb49123a09ab8609753b853aca6e965e7f3da","target":"graph","created_at":"2026-05-17T23:57:55Z","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":"Sentiment Analysis has seen much progress in the past two decades. For the past few years, neural network approaches, primarily RNNs and CNNs, have been the most successful for this task. Recently, a new category of neural networks, self-attention networks (SANs), have been created which utilizes the attention mechanism as the basic building block. Self-attention networks have been shown to be effective for sequence modeling tasks, while having no recurrence or convolutions. In this work we explore the effectiveness of the SANs for sentiment analysis. We demonstrate that SANs are superior in p","authors_text":"Artaches Ambartsoumian, Fred Popowich","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-12-19T10:21:20Z","title":"Self-Attention: A Better Building Block for Sentiment Analysis Neural Network Classifiers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.07860","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:ef0f9c50e8fb44c1b9be872c5aaf1d20494c045564700f5cbb3f26413c964f8c","target":"record","created_at":"2026-05-17T23:57:55Z","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":"5ce0aedde12410bd8db9e492cfe706dc30f0f541970134ba5f4645c1afd66216","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-12-19T10:21:20Z","title_canon_sha256":"0ec9a6e98a39c2c17a354d9513c5d0784c42fdb1de90e749db6b0ab84ee0bf32"},"schema_version":"1.0","source":{"id":"1812.07860","kind":"arxiv","version":1}},"canonical_sha256":"14147a163b3b0814fbaa9b1ca86b06993ff92b6db2ddc46e1b0f0473be650287","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"14147a163b3b0814fbaa9b1ca86b06993ff92b6db2ddc46e1b0f0473be650287","first_computed_at":"2026-05-17T23:57:55.716108Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:57:55.716108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/cUv67qWCyq1mBSblHWNxwtQOipSzNzX+TNWAHr/+lXrhkdSAWh8TW1TURe6c9GnRos4uoFck+okHtjwWgMMBw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:57:55.716698Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.07860","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ef0f9c50e8fb44c1b9be872c5aaf1d20494c045564700f5cbb3f26413c964f8c","sha256:3a12f64b71fc3191ca133492538eb49123a09ab8609753b853aca6e965e7f3da"],"state_sha256":"b2de0111c3a2282f0ed914475dcce82f2ae27173416a00c9efd173b6bbb78abc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d9/+expjdU0fDyxXKmHDSM4LYM2wpdXlpTa+U4yB/Hq69ZU/xz6YhCX0Cy+/yitvwu4IfFWKE5bwSp0iPhKWDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T12:26:53.215805Z","bundle_sha256":"d7f641390485e1019406e1f02e7f9886dd5c6556099cdaf994d358353667bcff"}}