{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:5NM5R3PNRNFSIJ4A3HNZMXTZO5","short_pith_number":"pith:5NM5R3PN","canonical_record":{"source":{"id":"1508.03826","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-08-16T14:12:17Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"2c3bbc32ad3fb47e27e8eeb63f22c686a81666480cf93ce00f66327f73e57dee","abstract_canon_sha256":"fec00eeacd1feccd9dac8d84989690d3c156b6c68b58a96d87c0edad73bc84fa"},"schema_version":"1.0"},"canonical_sha256":"eb59d8eded8b4b242780d9db965e7977781929165da7781c1534981158d1ae04","source":{"kind":"arxiv","id":"1508.03826","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1508.03826","created_at":"2026-05-18T01:35:15Z"},{"alias_kind":"arxiv_version","alias_value":"1508.03826v1","created_at":"2026-05-18T01:35:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1508.03826","created_at":"2026-05-18T01:35:15Z"},{"alias_kind":"pith_short_12","alias_value":"5NM5R3PNRNFS","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_16","alias_value":"5NM5R3PNRNFSIJ4A","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_8","alias_value":"5NM5R3PN","created_at":"2026-05-18T12:29:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:5NM5R3PNRNFSIJ4A3HNZMXTZO5","target":"record","payload":{"canonical_record":{"source":{"id":"1508.03826","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-08-16T14:12:17Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"2c3bbc32ad3fb47e27e8eeb63f22c686a81666480cf93ce00f66327f73e57dee","abstract_canon_sha256":"fec00eeacd1feccd9dac8d84989690d3c156b6c68b58a96d87c0edad73bc84fa"},"schema_version":"1.0"},"canonical_sha256":"eb59d8eded8b4b242780d9db965e7977781929165da7781c1534981158d1ae04","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:35:15.147387Z","signature_b64":"8AHMeqxpE27teXzehKgPQ/+wSNdLPrNBbEz9fEyo+ihGvNxdvwtKHLGAkh2ka50jhoUzdpTuyeHKjSj+sE0KAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb59d8eded8b4b242780d9db965e7977781929165da7781c1534981158d1ae04","last_reissued_at":"2026-05-18T01:35:15.146847Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:35:15.146847Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1508.03826","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":"2026-05-18T01:35:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/joifE0n1oOyxIpIhzGXcj6n97PipUC4xyeazsGjMujHEENpbgGnQoHW+obPy2OZZfLmJ3lkECXl2Wk40Z/NCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T05:25:15.804656Z"},"content_sha256":"7eae78ab07a006091ba7c6ab4bbb3bad937a7b7370daa35067dc1b0f9850c593","schema_version":"1.0","event_id":"sha256:7eae78ab07a006091ba7c6ab4bbb3bad937a7b7370daa35067dc1b0f9850c593"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:5NM5R3PNRNFSIJ4A3HNZMXTZO5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Generative Word Embedding Model and its Low Rank Positive Semidefinite Solution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CL","authors_text":"Chunyan Miao, Jun Zhu, Shaohua Li","submitted_at":"2015-08-16T14:12:17Z","abstract_excerpt":"Most existing word embedding methods can be categorized into Neural Embedding Models and Matrix Factorization (MF)-based methods. However some models are opaque to probabilistic interpretation, and MF-based methods, typically solved using Singular Value Decomposition (SVD), may incur loss of corpus information. In addition, it is desirable to incorporate global latent factors, such as topics, sentiments or writing styles, into the word embedding model. Since generative models provide a principled way to incorporate latent factors, we propose a generative word embedding model, which is easy to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1508.03826","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":"2026-05-18T01:35:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HsYok5mNTnjVDyNJb2HVK2sTJX9PHCs2JM+sT60qTviSS0JR72Mj5RI1GHZuCGX5A6Th2Q7RKIp3280Yr7oFDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T05:25:15.805355Z"},"content_sha256":"ebe4d065c61c26167d63feddc63ba146345b3841ec38d1f6fba45b213d9b54ba","schema_version":"1.0","event_id":"sha256:ebe4d065c61c26167d63feddc63ba146345b3841ec38d1f6fba45b213d9b54ba"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5NM5R3PNRNFSIJ4A3HNZMXTZO5/bundle.json","state_url":"https://pith.science/pith/5NM5R3PNRNFSIJ4A3HNZMXTZO5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5NM5R3PNRNFSIJ4A3HNZMXTZO5/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":"2026-05-31T05:25:15Z","links":{"resolver":"https://pith.science/pith/5NM5R3PNRNFSIJ4A3HNZMXTZO5","bundle":"https://pith.science/pith/5NM5R3PNRNFSIJ4A3HNZMXTZO5/bundle.json","state":"https://pith.science/pith/5NM5R3PNRNFSIJ4A3HNZMXTZO5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5NM5R3PNRNFSIJ4A3HNZMXTZO5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:5NM5R3PNRNFSIJ4A3HNZMXTZO5","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":"fec00eeacd1feccd9dac8d84989690d3c156b6c68b58a96d87c0edad73bc84fa","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-08-16T14:12:17Z","title_canon_sha256":"2c3bbc32ad3fb47e27e8eeb63f22c686a81666480cf93ce00f66327f73e57dee"},"schema_version":"1.0","source":{"id":"1508.03826","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1508.03826","created_at":"2026-05-18T01:35:15Z"},{"alias_kind":"arxiv_version","alias_value":"1508.03826v1","created_at":"2026-05-18T01:35:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1508.03826","created_at":"2026-05-18T01:35:15Z"},{"alias_kind":"pith_short_12","alias_value":"5NM5R3PNRNFS","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_16","alias_value":"5NM5R3PNRNFSIJ4A","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_8","alias_value":"5NM5R3PN","created_at":"2026-05-18T12:29:05Z"}],"graph_snapshots":[{"event_id":"sha256:ebe4d065c61c26167d63feddc63ba146345b3841ec38d1f6fba45b213d9b54ba","target":"graph","created_at":"2026-05-18T01:35:15Z","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":"Most existing word embedding methods can be categorized into Neural Embedding Models and Matrix Factorization (MF)-based methods. However some models are opaque to probabilistic interpretation, and MF-based methods, typically solved using Singular Value Decomposition (SVD), may incur loss of corpus information. In addition, it is desirable to incorporate global latent factors, such as topics, sentiments or writing styles, into the word embedding model. Since generative models provide a principled way to incorporate latent factors, we propose a generative word embedding model, which is easy to ","authors_text":"Chunyan Miao, Jun Zhu, Shaohua Li","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-08-16T14:12:17Z","title":"A Generative Word Embedding Model and its Low Rank Positive Semidefinite Solution"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1508.03826","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":"sha256:7eae78ab07a006091ba7c6ab4bbb3bad937a7b7370daa35067dc1b0f9850c593","target":"record","created_at":"2026-05-18T01:35:15Z","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":"fec00eeacd1feccd9dac8d84989690d3c156b6c68b58a96d87c0edad73bc84fa","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-08-16T14:12:17Z","title_canon_sha256":"2c3bbc32ad3fb47e27e8eeb63f22c686a81666480cf93ce00f66327f73e57dee"},"schema_version":"1.0","source":{"id":"1508.03826","kind":"arxiv","version":1}},"canonical_sha256":"eb59d8eded8b4b242780d9db965e7977781929165da7781c1534981158d1ae04","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"eb59d8eded8b4b242780d9db965e7977781929165da7781c1534981158d1ae04","first_computed_at":"2026-05-18T01:35:15.146847Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:35:15.146847Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8AHMeqxpE27teXzehKgPQ/+wSNdLPrNBbEz9fEyo+ihGvNxdvwtKHLGAkh2ka50jhoUzdpTuyeHKjSj+sE0KAw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:35:15.147387Z","signed_message":"canonical_sha256_bytes"},"source_id":"1508.03826","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7eae78ab07a006091ba7c6ab4bbb3bad937a7b7370daa35067dc1b0f9850c593","sha256:ebe4d065c61c26167d63feddc63ba146345b3841ec38d1f6fba45b213d9b54ba"],"state_sha256":"79b745a3b8942d638ef4e6657807556f3c9112bb777c1a23d0ddd5ee74fab057"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u/AAI9LoxCk+tG8ziRr6jX25a3FQq5IoPXQSGI4ONzkcrq+QHGOdZWsnNUTA5wuXVJTCAp+GLitG8BdkyB8hCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T05:25:15.809215Z","bundle_sha256":"370d40f795542727a86af2bee9c2f509d82980ae3e4c46a62cd27a7f9a037c63"}}