{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:I5X2N4IA6EUS4NNWFVHM2MPZOP","short_pith_number":"pith:I5X2N4IA","canonical_record":{"source":{"id":"1806.06349","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-06-17T08:44:55Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"9dcfc93d0aa5e7bc7d261d939ff3806111d2156da99a004831793e5d9b2eb615","abstract_canon_sha256":"1429cff081fc58fb7752a9960997d984143b8f9a2bf66bef7466fe707aab06b2"},"schema_version":"1.0"},"canonical_sha256":"476fa6f100f1292e35b62d4ecd31f973edec973ed9649e4ec85bed73986248a9","source":{"kind":"arxiv","id":"1806.06349","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.06349","created_at":"2026-05-18T00:13:02Z"},{"alias_kind":"arxiv_version","alias_value":"1806.06349v1","created_at":"2026-05-18T00:13:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.06349","created_at":"2026-05-18T00:13:02Z"},{"alias_kind":"pith_short_12","alias_value":"I5X2N4IA6EUS","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"I5X2N4IA6EUS4NNW","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"I5X2N4IA","created_at":"2026-05-18T12:32:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:I5X2N4IA6EUS4NNWFVHM2MPZOP","target":"record","payload":{"canonical_record":{"source":{"id":"1806.06349","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-06-17T08:44:55Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"9dcfc93d0aa5e7bc7d261d939ff3806111d2156da99a004831793e5d9b2eb615","abstract_canon_sha256":"1429cff081fc58fb7752a9960997d984143b8f9a2bf66bef7466fe707aab06b2"},"schema_version":"1.0"},"canonical_sha256":"476fa6f100f1292e35b62d4ecd31f973edec973ed9649e4ec85bed73986248a9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:02.780830Z","signature_b64":"bEGScNQUqDf1NEsEfSaK+ZKsRhfvGFONVqeIeUfbrfqJVzmv0IAHzU3ZOR9fwJPGFT+SNfAZhu59V56k4+4cDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"476fa6f100f1292e35b62d4ecd31f973edec973ed9649e4ec85bed73986248a9","last_reissued_at":"2026-05-18T00:13:02.779497Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:02.779497Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.06349","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":"2026-05-18T00:13:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QmRX4iiDe1xGILi8OufLOoipFx4T+JWaPG4QHYKVBkGmxtbFldwhabpUyoJ+0pBSMBMz2huvHK1pplZMInKzAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T05:34:37.961883Z"},"content_sha256":"95a9d1a3143ff5b6ac02f49474116fcdbaaf81948ac36866175e9e5b2b56f553","schema_version":"1.0","event_id":"sha256:95a9d1a3143ff5b6ac02f49474116fcdbaaf81948ac36866175e9e5b2b56f553"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:I5X2N4IA6EUS4NNWFVHM2MPZOP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Incorporating Chinese Characters of Words for Lexical Sememe Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Fen Lin, Hao Zhu, Huiming Jin, Leyu Lin, Maosong Sun, Ruobing Xie, Zhiyuan Liu","submitted_at":"2018-06-17T08:44:55Z","abstract_excerpt":"Sememes are minimum semantic units of concepts in human languages, such that each word sense is composed of one or multiple sememes. Words are usually manually annotated with their sememes by linguists, and form linguistic common-sense knowledge bases widely used in various NLP tasks. Recently, the lexical sememe prediction task has been introduced. It consists of automatically recommending sememes for words, which is expected to improve annotation efficiency and consistency. However, existing methods of lexical sememe prediction typically rely on the external context of words to represent the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.06349","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":"2026-05-18T00:13:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0JLwr3iFN+hryMzKHV/o54y2rmI3yKkCGcuv+Cwv9gkaoVQRQYE3OFlZM4NS9h2L3uDbobeR0KRhynK7MmsaAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T05:34:37.962234Z"},"content_sha256":"6943a479c861e26f64b8c9e174e00063fd4713d0f889db75387fd189f9133f6b","schema_version":"1.0","event_id":"sha256:6943a479c861e26f64b8c9e174e00063fd4713d0f889db75387fd189f9133f6b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I5X2N4IA6EUS4NNWFVHM2MPZOP/bundle.json","state_url":"https://pith.science/pith/I5X2N4IA6EUS4NNWFVHM2MPZOP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I5X2N4IA6EUS4NNWFVHM2MPZOP/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":"2026-06-03T05:34:37Z","links":{"resolver":"https://pith.science/pith/I5X2N4IA6EUS4NNWFVHM2MPZOP","bundle":"https://pith.science/pith/I5X2N4IA6EUS4NNWFVHM2MPZOP/bundle.json","state":"https://pith.science/pith/I5X2N4IA6EUS4NNWFVHM2MPZOP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I5X2N4IA6EUS4NNWFVHM2MPZOP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:I5X2N4IA6EUS4NNWFVHM2MPZOP","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":"1429cff081fc58fb7752a9960997d984143b8f9a2bf66bef7466fe707aab06b2","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-06-17T08:44:55Z","title_canon_sha256":"9dcfc93d0aa5e7bc7d261d939ff3806111d2156da99a004831793e5d9b2eb615"},"schema_version":"1.0","source":{"id":"1806.06349","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.06349","created_at":"2026-05-18T00:13:02Z"},{"alias_kind":"arxiv_version","alias_value":"1806.06349v1","created_at":"2026-05-18T00:13:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.06349","created_at":"2026-05-18T00:13:02Z"},{"alias_kind":"pith_short_12","alias_value":"I5X2N4IA6EUS","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"I5X2N4IA6EUS4NNW","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"I5X2N4IA","created_at":"2026-05-18T12:32:28Z"}],"graph_snapshots":[{"event_id":"sha256:6943a479c861e26f64b8c9e174e00063fd4713d0f889db75387fd189f9133f6b","target":"graph","created_at":"2026-05-18T00:13:02Z","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":"Sememes are minimum semantic units of concepts in human languages, such that each word sense is composed of one or multiple sememes. Words are usually manually annotated with their sememes by linguists, and form linguistic common-sense knowledge bases widely used in various NLP tasks. Recently, the lexical sememe prediction task has been introduced. It consists of automatically recommending sememes for words, which is expected to improve annotation efficiency and consistency. However, existing methods of lexical sememe prediction typically rely on the external context of words to represent the","authors_text":"Fen Lin, Hao Zhu, Huiming Jin, Leyu Lin, Maosong Sun, Ruobing Xie, Zhiyuan Liu","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-06-17T08:44:55Z","title":"Incorporating Chinese Characters of Words for Lexical Sememe Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.06349","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":"sha256:95a9d1a3143ff5b6ac02f49474116fcdbaaf81948ac36866175e9e5b2b56f553","target":"record","created_at":"2026-05-18T00:13:02Z","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":"1429cff081fc58fb7752a9960997d984143b8f9a2bf66bef7466fe707aab06b2","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-06-17T08:44:55Z","title_canon_sha256":"9dcfc93d0aa5e7bc7d261d939ff3806111d2156da99a004831793e5d9b2eb615"},"schema_version":"1.0","source":{"id":"1806.06349","kind":"arxiv","version":1}},"canonical_sha256":"476fa6f100f1292e35b62d4ecd31f973edec973ed9649e4ec85bed73986248a9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"476fa6f100f1292e35b62d4ecd31f973edec973ed9649e4ec85bed73986248a9","first_computed_at":"2026-05-18T00:13:02.779497Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:13:02.779497Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bEGScNQUqDf1NEsEfSaK+ZKsRhfvGFONVqeIeUfbrfqJVzmv0IAHzU3ZOR9fwJPGFT+SNfAZhu59V56k4+4cDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:13:02.780830Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.06349","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:95a9d1a3143ff5b6ac02f49474116fcdbaaf81948ac36866175e9e5b2b56f553","sha256:6943a479c861e26f64b8c9e174e00063fd4713d0f889db75387fd189f9133f6b"],"state_sha256":"eb6ae82bb34541fde63ef2409dd0ec5f299d6aa23746494abc1a24ae0e37a1a4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KuRTQk4qTIivYxrIumEyZEGXuCtPdOwhXLtQicmscz06foONnt7ZzWJYeYmqoAPnFNUCulR3bLe5lJyO+vioCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T05:34:37.964214Z","bundle_sha256":"ce4711432d62d819aba102f409729f3413536b9bd6822dfab655782a37f5da40"}}