{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:EVGL4OSSUECEYQG2ZUIJI7X4NK","short_pith_number":"pith:EVGL4OSS","canonical_record":{"source":{"id":"1311.2378","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-11-11T08:26:09Z","cross_cats_sorted":[],"title_canon_sha256":"0e3529aa66b5c26a2b2cfe0e2104f6c2afab451df967362aac7dc7717b6c3e42","abstract_canon_sha256":"f568dff649795ff333eec7d2520c5697544e7353e854f436be10eb338efb5ac0"},"schema_version":"1.0"},"canonical_sha256":"254cbe3a52a1044c40dacd10947efc6a9480db1c31401ccfdad0cdecd6b8970d","source":{"kind":"arxiv","id":"1311.2378","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1311.2378","created_at":"2026-05-18T03:07:32Z"},{"alias_kind":"arxiv_version","alias_value":"1311.2378v1","created_at":"2026-05-18T03:07:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1311.2378","created_at":"2026-05-18T03:07:32Z"},{"alias_kind":"pith_short_12","alias_value":"EVGL4OSSUECE","created_at":"2026-05-18T12:27:43Z"},{"alias_kind":"pith_short_16","alias_value":"EVGL4OSSUECEYQG2","created_at":"2026-05-18T12:27:43Z"},{"alias_kind":"pith_short_8","alias_value":"EVGL4OSS","created_at":"2026-05-18T12:27:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:EVGL4OSSUECEYQG2ZUIJI7X4NK","target":"record","payload":{"canonical_record":{"source":{"id":"1311.2378","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-11-11T08:26:09Z","cross_cats_sorted":[],"title_canon_sha256":"0e3529aa66b5c26a2b2cfe0e2104f6c2afab451df967362aac7dc7717b6c3e42","abstract_canon_sha256":"f568dff649795ff333eec7d2520c5697544e7353e854f436be10eb338efb5ac0"},"schema_version":"1.0"},"canonical_sha256":"254cbe3a52a1044c40dacd10947efc6a9480db1c31401ccfdad0cdecd6b8970d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:07:32.812985Z","signature_b64":"8HeiWCSRBczhUbem+1Wj3eWR2INtiqeb4p3479YdanGMvcMukeUsfM77EJA5zOGYQgyG9Squh54K7r3AVkc6Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"254cbe3a52a1044c40dacd10947efc6a9480db1c31401ccfdad0cdecd6b8970d","last_reissued_at":"2026-05-18T03:07:32.812431Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:07:32.812431Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1311.2378","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":"2026-05-18T03:07:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"631M4UwXaFtsinPpoG7t4LWgINlru05kPL++xTJAHj35Xa62jJD+eZlGj3B1RiRVMo0JHPtylAS1Mz+29TM1Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T14:19:39.734076Z"},"content_sha256":"85f8121064d9cca81e085a796b89269ccc2a37de33d47abc5ada760c08b1ec76","schema_version":"1.0","event_id":"sha256:85f8121064d9cca81e085a796b89269ccc2a37de33d47abc5ada760c08b1ec76"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:EVGL4OSSUECEYQG2ZUIJI7X4NK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An Empirical Evaluation of Sequence-Tagging Trainers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"P. Balamurugan, Shirish Shevade, S. S Keerthi, S. Sundararajan","submitted_at":"2013-11-11T08:26:09Z","abstract_excerpt":"The task of assigning label sequences to a set of observed sequences is common in computational linguistics. Several models for sequence labeling have been proposed over the last few years. Here, we focus on discriminative models for sequence labeling. Many batch and online (updating model parameters after visiting each example) learning algorithms have been proposed in the literature. On large datasets, online algorithms are preferred as batch learning methods are slow. These online algorithms were designed to solve either a primal or a dual problem. However, there has been no systematic comp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1311.2378","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":"2026-05-18T03:07:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lSvteIZgCiVBp+0q4bvNL4/DSlKDzscbwIY4aTowavu1+6aqFnV7dvbt+NePXGzddrQOxPvAFA9YQT40qtvpAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T14:19:39.734469Z"},"content_sha256":"8cc095ed08091703a47f5d19142042167e8447fc59f3a7a6ba64e82c07b35579","schema_version":"1.0","event_id":"sha256:8cc095ed08091703a47f5d19142042167e8447fc59f3a7a6ba64e82c07b35579"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EVGL4OSSUECEYQG2ZUIJI7X4NK/bundle.json","state_url":"https://pith.science/pith/EVGL4OSSUECEYQG2ZUIJI7X4NK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EVGL4OSSUECEYQG2ZUIJI7X4NK/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":"2026-06-02T14:19:39Z","links":{"resolver":"https://pith.science/pith/EVGL4OSSUECEYQG2ZUIJI7X4NK","bundle":"https://pith.science/pith/EVGL4OSSUECEYQG2ZUIJI7X4NK/bundle.json","state":"https://pith.science/pith/EVGL4OSSUECEYQG2ZUIJI7X4NK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EVGL4OSSUECEYQG2ZUIJI7X4NK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:EVGL4OSSUECEYQG2ZUIJI7X4NK","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":"f568dff649795ff333eec7d2520c5697544e7353e854f436be10eb338efb5ac0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-11-11T08:26:09Z","title_canon_sha256":"0e3529aa66b5c26a2b2cfe0e2104f6c2afab451df967362aac7dc7717b6c3e42"},"schema_version":"1.0","source":{"id":"1311.2378","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1311.2378","created_at":"2026-05-18T03:07:32Z"},{"alias_kind":"arxiv_version","alias_value":"1311.2378v1","created_at":"2026-05-18T03:07:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1311.2378","created_at":"2026-05-18T03:07:32Z"},{"alias_kind":"pith_short_12","alias_value":"EVGL4OSSUECE","created_at":"2026-05-18T12:27:43Z"},{"alias_kind":"pith_short_16","alias_value":"EVGL4OSSUECEYQG2","created_at":"2026-05-18T12:27:43Z"},{"alias_kind":"pith_short_8","alias_value":"EVGL4OSS","created_at":"2026-05-18T12:27:43Z"}],"graph_snapshots":[{"event_id":"sha256:8cc095ed08091703a47f5d19142042167e8447fc59f3a7a6ba64e82c07b35579","target":"graph","created_at":"2026-05-18T03:07:32Z","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":"The task of assigning label sequences to a set of observed sequences is common in computational linguistics. Several models for sequence labeling have been proposed over the last few years. Here, we focus on discriminative models for sequence labeling. Many batch and online (updating model parameters after visiting each example) learning algorithms have been proposed in the literature. On large datasets, online algorithms are preferred as batch learning methods are slow. These online algorithms were designed to solve either a primal or a dual problem. However, there has been no systematic comp","authors_text":"P. Balamurugan, Shirish Shevade, S. S Keerthi, S. Sundararajan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-11-11T08:26:09Z","title":"An Empirical Evaluation of Sequence-Tagging Trainers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1311.2378","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":"sha256:85f8121064d9cca81e085a796b89269ccc2a37de33d47abc5ada760c08b1ec76","target":"record","created_at":"2026-05-18T03:07:32Z","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":"f568dff649795ff333eec7d2520c5697544e7353e854f436be10eb338efb5ac0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-11-11T08:26:09Z","title_canon_sha256":"0e3529aa66b5c26a2b2cfe0e2104f6c2afab451df967362aac7dc7717b6c3e42"},"schema_version":"1.0","source":{"id":"1311.2378","kind":"arxiv","version":1}},"canonical_sha256":"254cbe3a52a1044c40dacd10947efc6a9480db1c31401ccfdad0cdecd6b8970d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"254cbe3a52a1044c40dacd10947efc6a9480db1c31401ccfdad0cdecd6b8970d","first_computed_at":"2026-05-18T03:07:32.812431Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:07:32.812431Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8HeiWCSRBczhUbem+1Wj3eWR2INtiqeb4p3479YdanGMvcMukeUsfM77EJA5zOGYQgyG9Squh54K7r3AVkc6Aw==","signature_status":"signed_v1","signed_at":"2026-05-18T03:07:32.812985Z","signed_message":"canonical_sha256_bytes"},"source_id":"1311.2378","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:85f8121064d9cca81e085a796b89269ccc2a37de33d47abc5ada760c08b1ec76","sha256:8cc095ed08091703a47f5d19142042167e8447fc59f3a7a6ba64e82c07b35579"],"state_sha256":"701f208f03a5447be133cdf99229def41a1098e227244ab6260f6d6bcc6315c9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WoZP8uOFYYP5JzingRTq6pz0JQSLAP9y2xLzBSKnrZM3UnJOAW5GqObkAP0e6gn+nvvhK2/JXBf2J6xQ8kPiBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T14:19:39.736508Z","bundle_sha256":"934435f387e5a14b719af4221351da0e54d6f7e50a9ce2eb551b66e86f60c7ed"}}