{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:7OJ5IXVQUDJ6GP5N5VLM56YQMA","short_pith_number":"pith:7OJ5IXVQ","canonical_record":{"source":{"id":"1802.05672","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-02-15T17:28:46Z","cross_cats_sorted":[],"title_canon_sha256":"a322f88988938fa46fe22fa74ef38a2c3ab59ee4e590e312bc931abe6d907ef8","abstract_canon_sha256":"d41fdc294d5f1cf8b959b4b44c1e4053c8f36833d7c0d52dd820d24bf0334765"},"schema_version":"1.0"},"canonical_sha256":"fb93d45eb0a0d3e33faded56cefb10602c9474bceea6f02b1a44bcf420870e70","source":{"kind":"arxiv","id":"1802.05672","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.05672","created_at":"2026-05-17T23:45:53Z"},{"alias_kind":"arxiv_version","alias_value":"1802.05672v1","created_at":"2026-05-17T23:45:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.05672","created_at":"2026-05-17T23:45:53Z"},{"alias_kind":"pith_short_12","alias_value":"7OJ5IXVQUDJ6","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"7OJ5IXVQUDJ6GP5N","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"7OJ5IXVQ","created_at":"2026-05-18T12:32:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:7OJ5IXVQUDJ6GP5N5VLM56YQMA","target":"record","payload":{"canonical_record":{"source":{"id":"1802.05672","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-02-15T17:28:46Z","cross_cats_sorted":[],"title_canon_sha256":"a322f88988938fa46fe22fa74ef38a2c3ab59ee4e590e312bc931abe6d907ef8","abstract_canon_sha256":"d41fdc294d5f1cf8b959b4b44c1e4053c8f36833d7c0d52dd820d24bf0334765"},"schema_version":"1.0"},"canonical_sha256":"fb93d45eb0a0d3e33faded56cefb10602c9474bceea6f02b1a44bcf420870e70","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:53.242154Z","signature_b64":"tEV2X4zRXMC9wuhNEQTMx43tBP+aari1mY4MnEFn3ycsGhqvGUQTbIK4Mjy84nie0GIFP9ishWpj+Z+2uxZJAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fb93d45eb0a0d3e33faded56cefb10602c9474bceea6f02b1a44bcf420870e70","last_reissued_at":"2026-05-17T23:45:53.241808Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:53.241808Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.05672","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":"2026-05-17T23:45:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QTtko9GL3EWX9yj8pLIZ8p7Ur+4NEJiFQDFQMlh4t4tcyJ5ygwHSVDfVz44dw3ZdQK5hwz2C2EZcBJdfaoTvAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T04:10:38.705553Z"},"content_sha256":"b4c640bf631b7c28cad13714c86be9a75802b75ec8286a2cd36958bf02a013d4","schema_version":"1.0","event_id":"sha256:b4c640bf631b7c28cad13714c86be9a75802b75ec8286a2cd36958bf02a013d4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:7OJ5IXVQUDJ6GP5N5VLM56YQMA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Event Nugget Detection with Forward-Backward Recurrent Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Liang Huang, Prasad Tadepalli, Reza Ghaeini, Xiaoli Z. Fern","submitted_at":"2018-02-15T17:28:46Z","abstract_excerpt":"Traditional event detection methods heavily rely on manually engineered rich features. Recent deep learning approaches alleviate this problem by automatic feature engineering. But such efforts, like tradition methods, have so far only focused on single-token event mentions, whereas in practice events can also be a phrase. We instead use forward-backward recurrent neural networks (FBRNNs) to detect events that can be either words or phrases. To the best our knowledge, this is one of the first efforts to handle multi-word events and also the first attempt to use RNNs for event detection. Experim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.05672","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":"2026-05-17T23:45:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CwECCTFYyvZvieLyQfXhmyz3nS516vkRWxuGWW25gT6RsSmTeJHWudMG+UYfIrtlR0ovi+1pUf2g3rPAlvveDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T04:10:38.706045Z"},"content_sha256":"2b8a1ca5663afd41d09bcf7bd1fc5510cdf68f4b80136a8a33078cf3f9797b64","schema_version":"1.0","event_id":"sha256:2b8a1ca5663afd41d09bcf7bd1fc5510cdf68f4b80136a8a33078cf3f9797b64"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7OJ5IXVQUDJ6GP5N5VLM56YQMA/bundle.json","state_url":"https://pith.science/pith/7OJ5IXVQUDJ6GP5N5VLM56YQMA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7OJ5IXVQUDJ6GP5N5VLM56YQMA/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":"2026-05-24T04:10:38Z","links":{"resolver":"https://pith.science/pith/7OJ5IXVQUDJ6GP5N5VLM56YQMA","bundle":"https://pith.science/pith/7OJ5IXVQUDJ6GP5N5VLM56YQMA/bundle.json","state":"https://pith.science/pith/7OJ5IXVQUDJ6GP5N5VLM56YQMA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7OJ5IXVQUDJ6GP5N5VLM56YQMA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:7OJ5IXVQUDJ6GP5N5VLM56YQMA","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":"d41fdc294d5f1cf8b959b4b44c1e4053c8f36833d7c0d52dd820d24bf0334765","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-02-15T17:28:46Z","title_canon_sha256":"a322f88988938fa46fe22fa74ef38a2c3ab59ee4e590e312bc931abe6d907ef8"},"schema_version":"1.0","source":{"id":"1802.05672","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.05672","created_at":"2026-05-17T23:45:53Z"},{"alias_kind":"arxiv_version","alias_value":"1802.05672v1","created_at":"2026-05-17T23:45:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.05672","created_at":"2026-05-17T23:45:53Z"},{"alias_kind":"pith_short_12","alias_value":"7OJ5IXVQUDJ6","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"7OJ5IXVQUDJ6GP5N","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"7OJ5IXVQ","created_at":"2026-05-18T12:32:11Z"}],"graph_snapshots":[{"event_id":"sha256:2b8a1ca5663afd41d09bcf7bd1fc5510cdf68f4b80136a8a33078cf3f9797b64","target":"graph","created_at":"2026-05-17T23:45:53Z","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":"Traditional event detection methods heavily rely on manually engineered rich features. Recent deep learning approaches alleviate this problem by automatic feature engineering. But such efforts, like tradition methods, have so far only focused on single-token event mentions, whereas in practice events can also be a phrase. We instead use forward-backward recurrent neural networks (FBRNNs) to detect events that can be either words or phrases. To the best our knowledge, this is one of the first efforts to handle multi-word events and also the first attempt to use RNNs for event detection. Experim","authors_text":"Liang Huang, Prasad Tadepalli, Reza Ghaeini, Xiaoli Z. Fern","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-02-15T17:28:46Z","title":"Event Nugget Detection with Forward-Backward Recurrent Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.05672","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:b4c640bf631b7c28cad13714c86be9a75802b75ec8286a2cd36958bf02a013d4","target":"record","created_at":"2026-05-17T23:45:53Z","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":"d41fdc294d5f1cf8b959b4b44c1e4053c8f36833d7c0d52dd820d24bf0334765","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-02-15T17:28:46Z","title_canon_sha256":"a322f88988938fa46fe22fa74ef38a2c3ab59ee4e590e312bc931abe6d907ef8"},"schema_version":"1.0","source":{"id":"1802.05672","kind":"arxiv","version":1}},"canonical_sha256":"fb93d45eb0a0d3e33faded56cefb10602c9474bceea6f02b1a44bcf420870e70","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fb93d45eb0a0d3e33faded56cefb10602c9474bceea6f02b1a44bcf420870e70","first_computed_at":"2026-05-17T23:45:53.241808Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:45:53.241808Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tEV2X4zRXMC9wuhNEQTMx43tBP+aari1mY4MnEFn3ycsGhqvGUQTbIK4Mjy84nie0GIFP9ishWpj+Z+2uxZJAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:45:53.242154Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.05672","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b4c640bf631b7c28cad13714c86be9a75802b75ec8286a2cd36958bf02a013d4","sha256:2b8a1ca5663afd41d09bcf7bd1fc5510cdf68f4b80136a8a33078cf3f9797b64"],"state_sha256":"fd8e5df11c42c113e778ec563d7068c129954020b361b958730636ce45524b71"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6pmHs2iWAWVNr05Kk1uDdQThknPTYD/1locj0SmLtlnOze0p4xtaJJA26VVYCBiS8DpxoP2LBfppex35DkhnDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-24T04:10:38.709481Z","bundle_sha256":"a90fe9923c16ec87d4d32517592371be21b2ff0ca5b997a66ac25c090bfca469"}}