{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:WOVDLOUWTHP3UHBWD6T7TOGAGC","short_pith_number":"pith:WOVDLOUW","canonical_record":{"source":{"id":"1804.10911","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-04-29T11:57:15Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"79f741e485b0c17f1f78a9ac783755bc6af20c0f1bef59aa2e0ec266a2a97bec","abstract_canon_sha256":"d4c961cf02e0b4549512fef86cd34a2916761103b0f32aa2abec6c393aac4935"},"schema_version":"1.0"},"canonical_sha256":"b3aa35ba9699dfba1c361fa7f9b8c030994e70a6e6decc86bd87b1ceb61fcb7b","source":{"kind":"arxiv","id":"1804.10911","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.10911","created_at":"2026-05-18T00:15:41Z"},{"alias_kind":"arxiv_version","alias_value":"1804.10911v2","created_at":"2026-05-18T00:15:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.10911","created_at":"2026-05-18T00:15:41Z"},{"alias_kind":"pith_short_12","alias_value":"WOVDLOUWTHP3","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"WOVDLOUWTHP3UHBW","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"WOVDLOUW","created_at":"2026-05-18T12:33:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:WOVDLOUWTHP3UHBWD6T7TOGAGC","target":"record","payload":{"canonical_record":{"source":{"id":"1804.10911","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-04-29T11:57:15Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"79f741e485b0c17f1f78a9ac783755bc6af20c0f1bef59aa2e0ec266a2a97bec","abstract_canon_sha256":"d4c961cf02e0b4549512fef86cd34a2916761103b0f32aa2abec6c393aac4935"},"schema_version":"1.0"},"canonical_sha256":"b3aa35ba9699dfba1c361fa7f9b8c030994e70a6e6decc86bd87b1ceb61fcb7b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:41.585744Z","signature_b64":"8oN5Wwqc/NoJ8Y8Z2/mtsb6XouE31IKtAaay0btKA0bFeGZx4Mt8Oean3s7tIIfmjIKxh9zXtEhDoYModR++Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b3aa35ba9699dfba1c361fa7f9b8c030994e70a6e6decc86bd87b1ceb61fcb7b","last_reissued_at":"2026-05-18T00:15:41.585065Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:41.585065Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1804.10911","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":"2026-05-18T00:15:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jj8VwA8kJurz7CeNpuLMaKoCyrsLiyq2lvjsZEL6o6wAIvoZOqaWT/NrCrhBA78O7tyBsVO0SezMTTux7SdUAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T22:15:59.413179Z"},"content_sha256":"0990d9909d64faa7e3b5cd267e710274a2c443dd3a7911fc3c4d8e7121e7ca09","schema_version":"1.0","event_id":"sha256:0990d9909d64faa7e3b5cd267e710274a2c443dd3a7911fc3c4d8e7121e7ca09"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:WOVDLOUWTHP3UHBWD6T7TOGAGC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Tree Search Algorithm for Sequence Labeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Jiafeng Guo, Jun Xu, Sheng Gao, Xueqi Cheng, Yadi Lao, Yanyan Lan","submitted_at":"2018-04-29T11:57:15Z","abstract_excerpt":"In this paper we propose a novel reinforcement learning based model for sequence tagging, referred to as MM-Tag. Inspired by the success and methodology of the AlphaGo Zero, MM-Tag formalizes the problem of sequence tagging with a Monte Carlo tree search (MCTS) enhanced Markov decision process (MDP) model, in which the time steps correspond to the positions of words in a sentence from left to right, and each action corresponds to assign a tag to a word. Two long short-term memory networks (LSTM) are used to summarize the past tag assignments and words in the sentence. Based on the outputs of L"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.10911","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":"2026-05-18T00:15:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BEmuwLURGXIpAimZXhmkBa4LBOZh/h2EgLZ4s4NDM1nbG61cw9IWi3bz5pQJLXvLgqHfnlc9RMKobbs02clLDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T22:15:59.413575Z"},"content_sha256":"a2cb9a34e1b2b1df87b0cc5de0cb390ffaa360f117e93cb132a05d75c8105f9a","schema_version":"1.0","event_id":"sha256:a2cb9a34e1b2b1df87b0cc5de0cb390ffaa360f117e93cb132a05d75c8105f9a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WOVDLOUWTHP3UHBWD6T7TOGAGC/bundle.json","state_url":"https://pith.science/pith/WOVDLOUWTHP3UHBWD6T7TOGAGC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WOVDLOUWTHP3UHBWD6T7TOGAGC/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":"2026-05-27T22:15:59Z","links":{"resolver":"https://pith.science/pith/WOVDLOUWTHP3UHBWD6T7TOGAGC","bundle":"https://pith.science/pith/WOVDLOUWTHP3UHBWD6T7TOGAGC/bundle.json","state":"https://pith.science/pith/WOVDLOUWTHP3UHBWD6T7TOGAGC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WOVDLOUWTHP3UHBWD6T7TOGAGC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:WOVDLOUWTHP3UHBWD6T7TOGAGC","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":"d4c961cf02e0b4549512fef86cd34a2916761103b0f32aa2abec6c393aac4935","cross_cats_sorted":["cs.IR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-04-29T11:57:15Z","title_canon_sha256":"79f741e485b0c17f1f78a9ac783755bc6af20c0f1bef59aa2e0ec266a2a97bec"},"schema_version":"1.0","source":{"id":"1804.10911","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.10911","created_at":"2026-05-18T00:15:41Z"},{"alias_kind":"arxiv_version","alias_value":"1804.10911v2","created_at":"2026-05-18T00:15:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.10911","created_at":"2026-05-18T00:15:41Z"},{"alias_kind":"pith_short_12","alias_value":"WOVDLOUWTHP3","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"WOVDLOUWTHP3UHBW","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"WOVDLOUW","created_at":"2026-05-18T12:33:01Z"}],"graph_snapshots":[{"event_id":"sha256:a2cb9a34e1b2b1df87b0cc5de0cb390ffaa360f117e93cb132a05d75c8105f9a","target":"graph","created_at":"2026-05-18T00:15:41Z","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":"In this paper we propose a novel reinforcement learning based model for sequence tagging, referred to as MM-Tag. Inspired by the success and methodology of the AlphaGo Zero, MM-Tag formalizes the problem of sequence tagging with a Monte Carlo tree search (MCTS) enhanced Markov decision process (MDP) model, in which the time steps correspond to the positions of words in a sentence from left to right, and each action corresponds to assign a tag to a word. Two long short-term memory networks (LSTM) are used to summarize the past tag assignments and words in the sentence. Based on the outputs of L","authors_text":"Jiafeng Guo, Jun Xu, Sheng Gao, Xueqi Cheng, Yadi Lao, Yanyan Lan","cross_cats":["cs.IR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-04-29T11:57:15Z","title":"A Tree Search Algorithm for Sequence Labeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.10911","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":"sha256:0990d9909d64faa7e3b5cd267e710274a2c443dd3a7911fc3c4d8e7121e7ca09","target":"record","created_at":"2026-05-18T00:15:41Z","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":"d4c961cf02e0b4549512fef86cd34a2916761103b0f32aa2abec6c393aac4935","cross_cats_sorted":["cs.IR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-04-29T11:57:15Z","title_canon_sha256":"79f741e485b0c17f1f78a9ac783755bc6af20c0f1bef59aa2e0ec266a2a97bec"},"schema_version":"1.0","source":{"id":"1804.10911","kind":"arxiv","version":2}},"canonical_sha256":"b3aa35ba9699dfba1c361fa7f9b8c030994e70a6e6decc86bd87b1ceb61fcb7b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b3aa35ba9699dfba1c361fa7f9b8c030994e70a6e6decc86bd87b1ceb61fcb7b","first_computed_at":"2026-05-18T00:15:41.585065Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:15:41.585065Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8oN5Wwqc/NoJ8Y8Z2/mtsb6XouE31IKtAaay0btKA0bFeGZx4Mt8Oean3s7tIIfmjIKxh9zXtEhDoYModR++Cw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:15:41.585744Z","signed_message":"canonical_sha256_bytes"},"source_id":"1804.10911","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0990d9909d64faa7e3b5cd267e710274a2c443dd3a7911fc3c4d8e7121e7ca09","sha256:a2cb9a34e1b2b1df87b0cc5de0cb390ffaa360f117e93cb132a05d75c8105f9a"],"state_sha256":"f39fffc0d5b30e07b8eea17f986d6054778307ada8265c5d8da191d8c88c1033"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pA7OqDXo/4ALcCHN7GaPdIhU65M76068us2PBFF72CAm3dXcqvlliZSqEBXupAC9iQzaKudqogHPxSu85PIgAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T22:15:59.416932Z","bundle_sha256":"9620ba54d99980249f5929001d25030026aa573781d5fc59e5924f21f8c7aa07"}}