{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:UGHSF3RZUMQUG6RQBIG4UBLJ5A","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":"351cbba42f8bee8f0b900c47e9b8ac6e4adc710d2f753ffb00f8fe55d55abdd9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2019-06-24T08:18:12Z","title_canon_sha256":"56a154857d632df2418d8b533cc0de523d6fd1770362b8a4d56002282d01b96b"},"schema_version":"1.0","source":{"id":"1906.10519","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.10519","created_at":"2026-05-17T23:42:16Z"},{"alias_kind":"arxiv_version","alias_value":"1906.10519v1","created_at":"2026-05-17T23:42:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.10519","created_at":"2026-05-17T23:42:16Z"},{"alias_kind":"pith_short_12","alias_value":"UGHSF3RZUMQU","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"UGHSF3RZUMQUG6RQ","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"UGHSF3RZ","created_at":"2026-05-18T12:33:30Z"}],"graph_snapshots":[{"event_id":"sha256:407443167bba640879ce764d92cad354f84c921d02382d225440885632cda47d","target":"graph","created_at":"2026-05-17T23:42:16Z","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 benefits from large, hand-annotated resources in order to train and test machine learning models, which are often data hungry. While some languages, e.g., English, have a vast array of these resources, most under-resourced languages do not, especially for fine-grained sentiment tasks, such as aspect-level or targeted sentiment analysis. To improve this situation, we propose a cross-lingual approach to sentiment analysis that is applicable to under-resourced languages and takes into account target-level information. This model incorporates sentiment information into bilingual","authors_text":"Jeremy Barnes, Roman Klinger","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2019-06-24T08:18:12Z","title":"Embedding Projection for Targeted Cross-Lingual Sentiment: Model Comparisons and a Real-World Study"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.10519","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":"sha256:449a9d582a2c4ba00badd3d75eb9915684a1452846b041a482bb9d1a1feeaebf","target":"record","created_at":"2026-05-17T23:42:16Z","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":"351cbba42f8bee8f0b900c47e9b8ac6e4adc710d2f753ffb00f8fe55d55abdd9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2019-06-24T08:18:12Z","title_canon_sha256":"56a154857d632df2418d8b533cc0de523d6fd1770362b8a4d56002282d01b96b"},"schema_version":"1.0","source":{"id":"1906.10519","kind":"arxiv","version":1}},"canonical_sha256":"a18f22ee39a321437a300a0dca0569e82409fb5e23a7a1acecd9127a0a75a593","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a18f22ee39a321437a300a0dca0569e82409fb5e23a7a1acecd9127a0a75a593","first_computed_at":"2026-05-17T23:42:16.136671Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:42:16.136671Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2oquV60dBK+5f/y980PU80CeMP1n7IZOYstKhIqMRPUXdz+nK9Ph5n3wszdas4SoY1ruu/1gd8hI6hO5ezlRAg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:42:16.137336Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.10519","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:449a9d582a2c4ba00badd3d75eb9915684a1452846b041a482bb9d1a1feeaebf","sha256:407443167bba640879ce764d92cad354f84c921d02382d225440885632cda47d"],"state_sha256":"2a7b24a41f3671d1df16fdde090468284fa8ec4416d755e2c6f48930e1fde8cb"}