{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:PQWNSLRLAZT3YH4QFIFNMYWB6Z","short_pith_number":"pith:PQWNSLRL","canonical_record":{"source":{"id":"1802.05818","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-02-16T02:00:10Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5b38d500bcd64591d80eec1cbc0169afbcc576efa03255e93427030c640f2183","abstract_canon_sha256":"0855811de7f911388d5ad7ccae90a6dcc39cffb2b4f70185809aa11b4a050f21"},"schema_version":"1.0"},"canonical_sha256":"7c2cd92e2b0667bc1f902a0ad662c1f678d4e5b143b213e1d330eac9e8a0bc5c","source":{"kind":"arxiv","id":"1802.05818","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.05818","created_at":"2026-05-18T00:23:10Z"},{"alias_kind":"arxiv_version","alias_value":"1802.05818v1","created_at":"2026-05-18T00:23:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.05818","created_at":"2026-05-18T00:23:10Z"},{"alias_kind":"pith_short_12","alias_value":"PQWNSLRLAZT3","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"PQWNSLRLAZT3YH4Q","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"PQWNSLRL","created_at":"2026-05-18T12:32:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:PQWNSLRLAZT3YH4QFIFNMYWB6Z","target":"record","payload":{"canonical_record":{"source":{"id":"1802.05818","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-02-16T02:00:10Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5b38d500bcd64591d80eec1cbc0169afbcc576efa03255e93427030c640f2183","abstract_canon_sha256":"0855811de7f911388d5ad7ccae90a6dcc39cffb2b4f70185809aa11b4a050f21"},"schema_version":"1.0"},"canonical_sha256":"7c2cd92e2b0667bc1f902a0ad662c1f678d4e5b143b213e1d330eac9e8a0bc5c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:23:10.950498Z","signature_b64":"5IVTn5x6HS8VqPUrdgFT5JKGlKzKT/TbhGfdMAlJutcFihb4/fkcKZK1W1dvdSVLD1X578TGEa5U0Vuo2XmtCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7c2cd92e2b0667bc1f902a0ad662c1f678d4e5b143b213e1d330eac9e8a0bc5c","last_reissued_at":"2026-05-18T00:23:10.949727Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:23:10.949727Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.05818","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":"2026-05-18T00:23:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0a1TJkW1YqgK3Mmsg+nJ21vM0p8FZYoF2L5r+pOs815cZcgLWO/vz5emq6akWMkhIuaax688l/ejUZooozaoAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T20:29:25.204858Z"},"content_sha256":"18a5cf247552b0cb59ff24aeaa6e1d7bd9f5c57327300e1f65a54d1b1b35029a","schema_version":"1.0","event_id":"sha256:18a5cf247552b0cb59ff24aeaa6e1d7bd9f5c57327300e1f65a54d1b1b35029a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:PQWNSLRLAZT3YH4QFIFNMYWB6Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Disentangling Aspect and Opinion Words in Target-based Sentiment Analysis using Lifelong Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Bing Liu, Mianwei Zhou, Sahisnu Mazumder, Shuai Wang, Yi Chang","submitted_at":"2018-02-16T02:00:10Z","abstract_excerpt":"Given a target name, which can be a product aspect or entity, identifying its aspect words and opinion words in a given corpus is a fine-grained task in target-based sentiment analysis (TSA). This task is challenging, especially when we have no labeled data and we want to perform it for any given domain. To address it, we propose a general two-stage approach. Stage one extracts/groups the target-related words (call t-words) for a given target. This is relatively easy as we can apply an existing semantics-based learning technique. Stage two separates the aspect and opinion words from the groupe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.05818","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":"2026-05-18T00:23:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g2d8B2aAYDwtnytU1ifSnquSD2PjGESwvsEl0lKbgGLxkQ0ZROUq/blw2vR63wWQ2NVjIJg6yyBHj9qGkuaNBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T20:29:25.205197Z"},"content_sha256":"dd2af295fb5bb9e61ff311eddb482e016b15d3c3248e200cf6494e59a819d67a","schema_version":"1.0","event_id":"sha256:dd2af295fb5bb9e61ff311eddb482e016b15d3c3248e200cf6494e59a819d67a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PQWNSLRLAZT3YH4QFIFNMYWB6Z/bundle.json","state_url":"https://pith.science/pith/PQWNSLRLAZT3YH4QFIFNMYWB6Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PQWNSLRLAZT3YH4QFIFNMYWB6Z/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":"2026-06-30T20:29:25Z","links":{"resolver":"https://pith.science/pith/PQWNSLRLAZT3YH4QFIFNMYWB6Z","bundle":"https://pith.science/pith/PQWNSLRLAZT3YH4QFIFNMYWB6Z/bundle.json","state":"https://pith.science/pith/PQWNSLRLAZT3YH4QFIFNMYWB6Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PQWNSLRLAZT3YH4QFIFNMYWB6Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:PQWNSLRLAZT3YH4QFIFNMYWB6Z","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":"0855811de7f911388d5ad7ccae90a6dcc39cffb2b4f70185809aa11b4a050f21","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-02-16T02:00:10Z","title_canon_sha256":"5b38d500bcd64591d80eec1cbc0169afbcc576efa03255e93427030c640f2183"},"schema_version":"1.0","source":{"id":"1802.05818","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.05818","created_at":"2026-05-18T00:23:10Z"},{"alias_kind":"arxiv_version","alias_value":"1802.05818v1","created_at":"2026-05-18T00:23:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.05818","created_at":"2026-05-18T00:23:10Z"},{"alias_kind":"pith_short_12","alias_value":"PQWNSLRLAZT3","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"PQWNSLRLAZT3YH4Q","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"PQWNSLRL","created_at":"2026-05-18T12:32:46Z"}],"graph_snapshots":[{"event_id":"sha256:dd2af295fb5bb9e61ff311eddb482e016b15d3c3248e200cf6494e59a819d67a","target":"graph","created_at":"2026-05-18T00:23:10Z","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":"Given a target name, which can be a product aspect or entity, identifying its aspect words and opinion words in a given corpus is a fine-grained task in target-based sentiment analysis (TSA). This task is challenging, especially when we have no labeled data and we want to perform it for any given domain. To address it, we propose a general two-stage approach. Stage one extracts/groups the target-related words (call t-words) for a given target. This is relatively easy as we can apply an existing semantics-based learning technique. Stage two separates the aspect and opinion words from the groupe","authors_text":"Bing Liu, Mianwei Zhou, Sahisnu Mazumder, Shuai Wang, Yi Chang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-02-16T02:00:10Z","title":"Disentangling Aspect and Opinion Words in Target-based Sentiment Analysis using Lifelong Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.05818","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":"sha256:18a5cf247552b0cb59ff24aeaa6e1d7bd9f5c57327300e1f65a54d1b1b35029a","target":"record","created_at":"2026-05-18T00:23:10Z","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":"0855811de7f911388d5ad7ccae90a6dcc39cffb2b4f70185809aa11b4a050f21","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-02-16T02:00:10Z","title_canon_sha256":"5b38d500bcd64591d80eec1cbc0169afbcc576efa03255e93427030c640f2183"},"schema_version":"1.0","source":{"id":"1802.05818","kind":"arxiv","version":1}},"canonical_sha256":"7c2cd92e2b0667bc1f902a0ad662c1f678d4e5b143b213e1d330eac9e8a0bc5c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7c2cd92e2b0667bc1f902a0ad662c1f678d4e5b143b213e1d330eac9e8a0bc5c","first_computed_at":"2026-05-18T00:23:10.949727Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:23:10.949727Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5IVTn5x6HS8VqPUrdgFT5JKGlKzKT/TbhGfdMAlJutcFihb4/fkcKZK1W1dvdSVLD1X578TGEa5U0Vuo2XmtCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:23:10.950498Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.05818","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:18a5cf247552b0cb59ff24aeaa6e1d7bd9f5c57327300e1f65a54d1b1b35029a","sha256:dd2af295fb5bb9e61ff311eddb482e016b15d3c3248e200cf6494e59a819d67a"],"state_sha256":"38e7f6d834563f21853ef1cdbea85970100abb5ce17ab388be10b6f82d4551f4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aq9/xz6kVt9oJ6VxNgMNjg793Irmq7xAw1h6sqsdC7wZ2m7EwPa/NuKHRlGamdmzF8xGZiZv+mceEcg6XELlAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-30T20:29:25.207492Z","bundle_sha256":"a974183eebe452f4144c0da4336fd75a01040ebd3d084be8f51bbff1a7f96755"}}