{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:AZGRQ4ZL2OQCMEQ666BHBEL4Z2","short_pith_number":"pith:AZGRQ4ZL","canonical_record":{"source":{"id":"1611.05384","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-11-16T17:47:57Z","cross_cats_sorted":[],"title_canon_sha256":"1e9a3a74006982c52bc910f6e99f03bdb61f162175f0463d178359151bba5da8","abstract_canon_sha256":"f24c4b994bb284f9da74c6c913ab39b8888c1ddd232adbd11dccb0e8d560bf2a"},"schema_version":"1.0"},"canonical_sha256":"064d18732bd3a026121ef78270917cce8cb22e5011ea5ddff9db479f8b0ebbf4","source":{"kind":"arxiv","id":"1611.05384","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.05384","created_at":"2026-05-18T00:41:07Z"},{"alias_kind":"arxiv_version","alias_value":"1611.05384v2","created_at":"2026-05-18T00:41:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.05384","created_at":"2026-05-18T00:41:07Z"},{"alias_kind":"pith_short_12","alias_value":"AZGRQ4ZL2OQC","created_at":"2026-05-18T12:30:07Z"},{"alias_kind":"pith_short_16","alias_value":"AZGRQ4ZL2OQCMEQ6","created_at":"2026-05-18T12:30:07Z"},{"alias_kind":"pith_short_8","alias_value":"AZGRQ4ZL","created_at":"2026-05-18T12:30:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:AZGRQ4ZL2OQCMEQ666BHBEL4Z2","target":"record","payload":{"canonical_record":{"source":{"id":"1611.05384","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-11-16T17:47:57Z","cross_cats_sorted":[],"title_canon_sha256":"1e9a3a74006982c52bc910f6e99f03bdb61f162175f0463d178359151bba5da8","abstract_canon_sha256":"f24c4b994bb284f9da74c6c913ab39b8888c1ddd232adbd11dccb0e8d560bf2a"},"schema_version":"1.0"},"canonical_sha256":"064d18732bd3a026121ef78270917cce8cb22e5011ea5ddff9db479f8b0ebbf4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:41:07.766773Z","signature_b64":"M5FAjFRgClcWJ7YwiFzSmjsmMFt13u4K8oULJ30fyFQD4M0uP5uXWwALIeV/WIo1qqUKBpNzIlYtAcSSzA9ABg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"064d18732bd3a026121ef78270917cce8cb22e5011ea5ddff9db479f8b0ebbf4","last_reissued_at":"2026-05-18T00:41:07.766198Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:41:07.766198Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1611.05384","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:41:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WMBLMLPwYMvXCc7Spy27qx5cf/URk4PypcKkYNLKMLbqJp8zLbAqARAv7dd1sOXD1JAEjqUMpv6JDGSfcDmfAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T12:22:27.103504Z"},"content_sha256":"f00bc5b9a302aa45b9588edf18832f84af8555b6062ea7b2e183b5a85b8145f1","schema_version":"1.0","event_id":"sha256:f00bc5b9a302aa45b9588edf18832f84af8555b6062ea7b2e183b5a85b8145f1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:AZGRQ4ZL2OQCMEQ666BHBEL4Z2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Xinchi Chen, Xipeng Qiu, Xuanjing Huang","submitted_at":"2016-11-16T17:47:57Z","abstract_excerpt":"Recently, neural network models for natural language processing tasks have been increasingly focused on for their ability of alleviating the burden of manual feature engineering. However, the previous neural models cannot extract the complicated feature compositions as the traditional methods with discrete features. In this work, we propose a feature-enriched neural model for joint Chinese word segmentation and part-of-speech tagging task. Specifically, to simulate the feature templates of traditional discrete feature based models, we use different filters to model the complex compositional fe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.05384","kind":"arxiv","version":2},"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":"2026-05-18T00:41:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FwEblOKOZ7UpKdykeNTD97w5CpxS9YbCrILYK/VTsxP+lEqfezCwIYgJMecYimeI6VAti/UpI/slVpooF127DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T12:22:27.104244Z"},"content_sha256":"3ce81b7c407018babe05572b2065aa496eee623f0393bbf9358426bfa4f869ce","schema_version":"1.0","event_id":"sha256:3ce81b7c407018babe05572b2065aa496eee623f0393bbf9358426bfa4f869ce"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AZGRQ4ZL2OQCMEQ666BHBEL4Z2/bundle.json","state_url":"https://pith.science/pith/AZGRQ4ZL2OQCMEQ666BHBEL4Z2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AZGRQ4ZL2OQCMEQ666BHBEL4Z2/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":"2026-05-23T12:22:27Z","links":{"resolver":"https://pith.science/pith/AZGRQ4ZL2OQCMEQ666BHBEL4Z2","bundle":"https://pith.science/pith/AZGRQ4ZL2OQCMEQ666BHBEL4Z2/bundle.json","state":"https://pith.science/pith/AZGRQ4ZL2OQCMEQ666BHBEL4Z2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AZGRQ4ZL2OQCMEQ666BHBEL4Z2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:AZGRQ4ZL2OQCMEQ666BHBEL4Z2","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":"f24c4b994bb284f9da74c6c913ab39b8888c1ddd232adbd11dccb0e8d560bf2a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-11-16T17:47:57Z","title_canon_sha256":"1e9a3a74006982c52bc910f6e99f03bdb61f162175f0463d178359151bba5da8"},"schema_version":"1.0","source":{"id":"1611.05384","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.05384","created_at":"2026-05-18T00:41:07Z"},{"alias_kind":"arxiv_version","alias_value":"1611.05384v2","created_at":"2026-05-18T00:41:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.05384","created_at":"2026-05-18T00:41:07Z"},{"alias_kind":"pith_short_12","alias_value":"AZGRQ4ZL2OQC","created_at":"2026-05-18T12:30:07Z"},{"alias_kind":"pith_short_16","alias_value":"AZGRQ4ZL2OQCMEQ6","created_at":"2026-05-18T12:30:07Z"},{"alias_kind":"pith_short_8","alias_value":"AZGRQ4ZL","created_at":"2026-05-18T12:30:07Z"}],"graph_snapshots":[{"event_id":"sha256:3ce81b7c407018babe05572b2065aa496eee623f0393bbf9358426bfa4f869ce","target":"graph","created_at":"2026-05-18T00:41:07Z","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":"Recently, neural network models for natural language processing tasks have been increasingly focused on for their ability of alleviating the burden of manual feature engineering. However, the previous neural models cannot extract the complicated feature compositions as the traditional methods with discrete features. In this work, we propose a feature-enriched neural model for joint Chinese word segmentation and part-of-speech tagging task. Specifically, to simulate the feature templates of traditional discrete feature based models, we use different filters to model the complex compositional fe","authors_text":"Xinchi Chen, Xipeng Qiu, Xuanjing Huang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-11-16T17:47:57Z","title":"A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.05384","kind":"arxiv","version":2},"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:f00bc5b9a302aa45b9588edf18832f84af8555b6062ea7b2e183b5a85b8145f1","target":"record","created_at":"2026-05-18T00:41:07Z","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":"f24c4b994bb284f9da74c6c913ab39b8888c1ddd232adbd11dccb0e8d560bf2a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-11-16T17:47:57Z","title_canon_sha256":"1e9a3a74006982c52bc910f6e99f03bdb61f162175f0463d178359151bba5da8"},"schema_version":"1.0","source":{"id":"1611.05384","kind":"arxiv","version":2}},"canonical_sha256":"064d18732bd3a026121ef78270917cce8cb22e5011ea5ddff9db479f8b0ebbf4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"064d18732bd3a026121ef78270917cce8cb22e5011ea5ddff9db479f8b0ebbf4","first_computed_at":"2026-05-18T00:41:07.766198Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:41:07.766198Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"M5FAjFRgClcWJ7YwiFzSmjsmMFt13u4K8oULJ30fyFQD4M0uP5uXWwALIeV/WIo1qqUKBpNzIlYtAcSSzA9ABg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:41:07.766773Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.05384","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f00bc5b9a302aa45b9588edf18832f84af8555b6062ea7b2e183b5a85b8145f1","sha256:3ce81b7c407018babe05572b2065aa496eee623f0393bbf9358426bfa4f869ce"],"state_sha256":"d8ffc65a8e12314a25e98f7bf20d0834c0731c074c7787c08876ba2f13b588db"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZP+28UDeMCAqpToKNbAK7BpevMpybET3d+KTkg9JgPPhvpx+1Nv10/46hCk02TNXKEO5mmsCDwwinef5RE7iBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T12:22:27.108547Z","bundle_sha256":"a4155a0f807d80f6162cb2e1a0f12b3c12bcc6e6baed92212d96ef70e0db3259"}}