{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:DDR5TQJVYWUD5UH457UCDP4MCV","short_pith_number":"pith:DDR5TQJV","canonical_record":{"source":{"id":"2507.09485","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-07-13T04:07:07Z","cross_cats_sorted":[],"title_canon_sha256":"69af8732e4ebc119f3cbb8b536399a7b77d81aec8827d4fe0c1b9145e0d9ce64","abstract_canon_sha256":"49385cbfdf19e57705da49b26065452c2885dddd81282d9f2a14aef9f5a61ab8"},"schema_version":"1.0"},"canonical_sha256":"18e3d9c135c5a83ed0fcefe821bf8c154fbd04c69039af319168aa2190b8aec0","source":{"kind":"arxiv","id":"2507.09485","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.09485","created_at":"2026-07-05T11:36:09Z"},{"alias_kind":"arxiv_version","alias_value":"2507.09485v1","created_at":"2026-07-05T11:36:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.09485","created_at":"2026-07-05T11:36:09Z"},{"alias_kind":"pith_short_12","alias_value":"DDR5TQJVYWUD","created_at":"2026-07-05T11:36:09Z"},{"alias_kind":"pith_short_16","alias_value":"DDR5TQJVYWUD5UH4","created_at":"2026-07-05T11:36:09Z"},{"alias_kind":"pith_short_8","alias_value":"DDR5TQJV","created_at":"2026-07-05T11:36:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:DDR5TQJVYWUD5UH457UCDP4MCV","target":"record","payload":{"canonical_record":{"source":{"id":"2507.09485","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-07-13T04:07:07Z","cross_cats_sorted":[],"title_canon_sha256":"69af8732e4ebc119f3cbb8b536399a7b77d81aec8827d4fe0c1b9145e0d9ce64","abstract_canon_sha256":"49385cbfdf19e57705da49b26065452c2885dddd81282d9f2a14aef9f5a61ab8"},"schema_version":"1.0"},"canonical_sha256":"18e3d9c135c5a83ed0fcefe821bf8c154fbd04c69039af319168aa2190b8aec0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:36:09.820171Z","signature_b64":"xbkT8ONJt0UHs3sawDWJQER7V7ZOyNwr+Wa4IUH8y4NXAsx3i3kdvcyNo/oJdPkc4wdJvJ6a2Tl0pTlN6lO9Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"18e3d9c135c5a83ed0fcefe821bf8c154fbd04c69039af319168aa2190b8aec0","last_reissued_at":"2026-07-05T11:36:09.819662Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:36:09.819662Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.09485","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":"2026-07-05T11:36:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JGTep6Zawt42t/+SDfENqfOenzKOfRmkmSQwQalaqmA+rkVKbBqOa2pVKT8guMB64DDFrVFWVfoJtSD0M3UJCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T01:40:34.685656Z"},"content_sha256":"4f8826af6892769797554c11dd8f171b09c529d8c041fdfff8489b7b664ecb20","schema_version":"1.0","event_id":"sha256:4f8826af6892769797554c11dd8f171b09c529d8c041fdfff8489b7b664ecb20"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:DDR5TQJVYWUD5UH457UCDP4MCV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Balanced Training Data Augmentation for Aspect-Based Sentiment Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Junjie Liu, Yan Song, Yuanhe Tian","submitted_at":"2025-07-13T04:07:07Z","abstract_excerpt":"Aspect-based sentiment analysis (ABSA) is a crucial fine-grained task in social media scenarios to identify the sentiment polarity of specific aspect terms in a sentence. Although many existing studies leverage large language models (LLMs) to perform ABSA due to their strong context understanding capabilities, they still face challenges to learn the context information in the running text because of the short text, as well as the small and unbalanced labeled training data, where most data are labeled with positive sentiment. Data augmentation (DA) is a feasible strategy for providing richer co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.09485","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2507.09485/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T11:36:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ASFEkrASrvD7mPIbFNDvhPiRFJe2TclT2ZfRNQZVyEqeHHjXHcVhFpdqBOmaKvR297Ilr0P+l5cVMCK8VI6SCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T01:40:34.686158Z"},"content_sha256":"9a9a3451c68aa6352883668eb41d0b827ed7c9fe6426e522af005529558dd391","schema_version":"1.0","event_id":"sha256:9a9a3451c68aa6352883668eb41d0b827ed7c9fe6426e522af005529558dd391"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DDR5TQJVYWUD5UH457UCDP4MCV/bundle.json","state_url":"https://pith.science/pith/DDR5TQJVYWUD5UH457UCDP4MCV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DDR5TQJVYWUD5UH457UCDP4MCV/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":"2026-08-09T01:40:34Z","links":{"resolver":"https://pith.science/pith/DDR5TQJVYWUD5UH457UCDP4MCV","bundle":"https://pith.science/pith/DDR5TQJVYWUD5UH457UCDP4MCV/bundle.json","state":"https://pith.science/pith/DDR5TQJVYWUD5UH457UCDP4MCV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DDR5TQJVYWUD5UH457UCDP4MCV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:DDR5TQJVYWUD5UH457UCDP4MCV","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":"49385cbfdf19e57705da49b26065452c2885dddd81282d9f2a14aef9f5a61ab8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-07-13T04:07:07Z","title_canon_sha256":"69af8732e4ebc119f3cbb8b536399a7b77d81aec8827d4fe0c1b9145e0d9ce64"},"schema_version":"1.0","source":{"id":"2507.09485","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.09485","created_at":"2026-07-05T11:36:09Z"},{"alias_kind":"arxiv_version","alias_value":"2507.09485v1","created_at":"2026-07-05T11:36:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.09485","created_at":"2026-07-05T11:36:09Z"},{"alias_kind":"pith_short_12","alias_value":"DDR5TQJVYWUD","created_at":"2026-07-05T11:36:09Z"},{"alias_kind":"pith_short_16","alias_value":"DDR5TQJVYWUD5UH4","created_at":"2026-07-05T11:36:09Z"},{"alias_kind":"pith_short_8","alias_value":"DDR5TQJV","created_at":"2026-07-05T11:36:09Z"}],"graph_snapshots":[{"event_id":"sha256:9a9a3451c68aa6352883668eb41d0b827ed7c9fe6426e522af005529558dd391","target":"graph","created_at":"2026-07-05T11:36:09Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2507.09485/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Aspect-based sentiment analysis (ABSA) is a crucial fine-grained task in social media scenarios to identify the sentiment polarity of specific aspect terms in a sentence. Although many existing studies leverage large language models (LLMs) to perform ABSA due to their strong context understanding capabilities, they still face challenges to learn the context information in the running text because of the short text, as well as the small and unbalanced labeled training data, where most data are labeled with positive sentiment. Data augmentation (DA) is a feasible strategy for providing richer co","authors_text":"Junjie Liu, Yan Song, Yuanhe Tian","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-07-13T04:07:07Z","title":"Balanced Training Data Augmentation for Aspect-Based Sentiment Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.09485","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":"sha256:4f8826af6892769797554c11dd8f171b09c529d8c041fdfff8489b7b664ecb20","target":"record","created_at":"2026-07-05T11:36:09Z","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":"49385cbfdf19e57705da49b26065452c2885dddd81282d9f2a14aef9f5a61ab8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-07-13T04:07:07Z","title_canon_sha256":"69af8732e4ebc119f3cbb8b536399a7b77d81aec8827d4fe0c1b9145e0d9ce64"},"schema_version":"1.0","source":{"id":"2507.09485","kind":"arxiv","version":1}},"canonical_sha256":"18e3d9c135c5a83ed0fcefe821bf8c154fbd04c69039af319168aa2190b8aec0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"18e3d9c135c5a83ed0fcefe821bf8c154fbd04c69039af319168aa2190b8aec0","first_computed_at":"2026-07-05T11:36:09.819662Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:36:09.819662Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xbkT8ONJt0UHs3sawDWJQER7V7ZOyNwr+Wa4IUH8y4NXAsx3i3kdvcyNo/oJdPkc4wdJvJ6a2Tl0pTlN6lO9Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:36:09.820171Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.09485","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4f8826af6892769797554c11dd8f171b09c529d8c041fdfff8489b7b664ecb20","sha256:9a9a3451c68aa6352883668eb41d0b827ed7c9fe6426e522af005529558dd391"],"state_sha256":"65666cd7b43bb265753cbde6fbbffa1fb73306e5c1652b03b17d53d33db56904"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OzziVFh1EC837bdFJOMPFCTNN8o5Ex8zif4aZM+DIuEtiu/LAAOKYT6soNyk7qxqWZkoJsFzDMsWOuzWttWoCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T01:40:34.690607Z","bundle_sha256":"23fac5738f025aaba93682ff3a4bbb4a0f332e338cfeaa2059a895485c835e42"}}