{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:WVEGMW53IEKVFWEUQ4NI7ODQG6","short_pith_number":"pith:WVEGMW53","canonical_record":{"source":{"id":"1902.08856","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2019-02-23T22:17:08Z","cross_cats_sorted":[],"title_canon_sha256":"5a5f3ed72f245c432248195048264e849b70bd5d717ef730cebd876ed018e645","abstract_canon_sha256":"ebd5737f797349cf8ca265bbfe4e27c8d12b5f8046f57d4ea66cd11d4e1a5f99"},"schema_version":"1.0"},"canonical_sha256":"b548665bbb411552d894871a8fb87037bb5fc6d0eb4f382b8c2726c3e6ae20e2","source":{"kind":"arxiv","id":"1902.08856","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.08856","created_at":"2026-05-17T23:52:49Z"},{"alias_kind":"arxiv_version","alias_value":"1902.08856v1","created_at":"2026-05-17T23:52:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.08856","created_at":"2026-05-17T23:52:49Z"},{"alias_kind":"pith_short_12","alias_value":"WVEGMW53IEKV","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"WVEGMW53IEKVFWEU","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"WVEGMW53","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:WVEGMW53IEKVFWEUQ4NI7ODQG6","target":"record","payload":{"canonical_record":{"source":{"id":"1902.08856","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2019-02-23T22:17:08Z","cross_cats_sorted":[],"title_canon_sha256":"5a5f3ed72f245c432248195048264e849b70bd5d717ef730cebd876ed018e645","abstract_canon_sha256":"ebd5737f797349cf8ca265bbfe4e27c8d12b5f8046f57d4ea66cd11d4e1a5f99"},"schema_version":"1.0"},"canonical_sha256":"b548665bbb411552d894871a8fb87037bb5fc6d0eb4f382b8c2726c3e6ae20e2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:49.679267Z","signature_b64":"LtN9WjoNAf9m7LcQuzyypxfCIQRZ3qT6/807P75AB8CGIp5SI08/yOSCEqKUp0B6zXMMs48zvrEWZk41/KNrAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b548665bbb411552d894871a8fb87037bb5fc6d0eb4f382b8c2726c3e6ae20e2","last_reissued_at":"2026-05-17T23:52:49.678553Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:49.678553Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1902.08856","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":"2026-05-17T23:52:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y/j8nBmPI4kGxJPIzuE3q9RamRjkt0ABjgdbufroXSIvvh+1qP/2z3G8XUOy3jJEOrnIezIpdevKpC0lkthhAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T13:32:24.453254Z"},"content_sha256":"5d33d3a13dffe497024ab29563051125bb0ef6ceee2ad39d7a94eb0943564e28","schema_version":"1.0","event_id":"sha256:5d33d3a13dffe497024ab29563051125bb0ef6ceee2ad39d7a94eb0943564e28"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:WVEGMW53IEKVFWEUQ4NI7ODQG6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ABI Neural Ensemble Model for Gender Prediction Adapt Bar-Ilan Submission for the CLIN29 Shared Task on Gender Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alberto Poncelas, Amit Moryossef, Andy Way, Dimitar Shterionov, Eva Vanmassenhove","submitted_at":"2019-02-23T22:17:08Z","abstract_excerpt":"We present our system for the CLIN29 shared task on cross-genre gender detection for Dutch. We experimented with a multitude of neural models (CNN, RNN, LSTM, etc.), more \"traditional\" models (SVM, RF, LogReg, etc.), different feature sets as well as data pre-processing. The final results suggested that using tokenized, non-lowercased data works best for most of the neural models, while a combination of word clusters, character trigrams and word lists showed to be most beneficial for the majority of the more \"traditional\" (that is, non-neural) models, beating features used in previous tasks su"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.08856","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":"2026-05-17T23:52:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eg4VCldMsjrXA5psYHdHLQGVkhfoh3EmL+9m3gsbZVXAbkZC2BBCOAf+7xm0SzLFy2eMFQd9+SdNqHGkAJq6DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T13:32:24.453632Z"},"content_sha256":"3cefa3c96a3b564ef4b062cfcda459335ba7c95d61ea86ea4d5092d23b1e39c7","schema_version":"1.0","event_id":"sha256:3cefa3c96a3b564ef4b062cfcda459335ba7c95d61ea86ea4d5092d23b1e39c7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WVEGMW53IEKVFWEUQ4NI7ODQG6/bundle.json","state_url":"https://pith.science/pith/WVEGMW53IEKVFWEUQ4NI7ODQG6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WVEGMW53IEKVFWEUQ4NI7ODQG6/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":"2026-06-04T13:32:24Z","links":{"resolver":"https://pith.science/pith/WVEGMW53IEKVFWEUQ4NI7ODQG6","bundle":"https://pith.science/pith/WVEGMW53IEKVFWEUQ4NI7ODQG6/bundle.json","state":"https://pith.science/pith/WVEGMW53IEKVFWEUQ4NI7ODQG6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WVEGMW53IEKVFWEUQ4NI7ODQG6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:WVEGMW53IEKVFWEUQ4NI7ODQG6","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":"ebd5737f797349cf8ca265bbfe4e27c8d12b5f8046f57d4ea66cd11d4e1a5f99","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2019-02-23T22:17:08Z","title_canon_sha256":"5a5f3ed72f245c432248195048264e849b70bd5d717ef730cebd876ed018e645"},"schema_version":"1.0","source":{"id":"1902.08856","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.08856","created_at":"2026-05-17T23:52:49Z"},{"alias_kind":"arxiv_version","alias_value":"1902.08856v1","created_at":"2026-05-17T23:52:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.08856","created_at":"2026-05-17T23:52:49Z"},{"alias_kind":"pith_short_12","alias_value":"WVEGMW53IEKV","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"WVEGMW53IEKVFWEU","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"WVEGMW53","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:3cefa3c96a3b564ef4b062cfcda459335ba7c95d61ea86ea4d5092d23b1e39c7","target":"graph","created_at":"2026-05-17T23:52:49Z","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":"We present our system for the CLIN29 shared task on cross-genre gender detection for Dutch. We experimented with a multitude of neural models (CNN, RNN, LSTM, etc.), more \"traditional\" models (SVM, RF, LogReg, etc.), different feature sets as well as data pre-processing. The final results suggested that using tokenized, non-lowercased data works best for most of the neural models, while a combination of word clusters, character trigrams and word lists showed to be most beneficial for the majority of the more \"traditional\" (that is, non-neural) models, beating features used in previous tasks su","authors_text":"Alberto Poncelas, Amit Moryossef, Andy Way, Dimitar Shterionov, Eva Vanmassenhove","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2019-02-23T22:17:08Z","title":"ABI Neural Ensemble Model for Gender Prediction Adapt Bar-Ilan Submission for the CLIN29 Shared Task on Gender Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.08856","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":"sha256:5d33d3a13dffe497024ab29563051125bb0ef6ceee2ad39d7a94eb0943564e28","target":"record","created_at":"2026-05-17T23:52:49Z","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":"ebd5737f797349cf8ca265bbfe4e27c8d12b5f8046f57d4ea66cd11d4e1a5f99","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2019-02-23T22:17:08Z","title_canon_sha256":"5a5f3ed72f245c432248195048264e849b70bd5d717ef730cebd876ed018e645"},"schema_version":"1.0","source":{"id":"1902.08856","kind":"arxiv","version":1}},"canonical_sha256":"b548665bbb411552d894871a8fb87037bb5fc6d0eb4f382b8c2726c3e6ae20e2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b548665bbb411552d894871a8fb87037bb5fc6d0eb4f382b8c2726c3e6ae20e2","first_computed_at":"2026-05-17T23:52:49.678553Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:52:49.678553Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LtN9WjoNAf9m7LcQuzyypxfCIQRZ3qT6/807P75AB8CGIp5SI08/yOSCEqKUp0B6zXMMs48zvrEWZk41/KNrAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:52:49.679267Z","signed_message":"canonical_sha256_bytes"},"source_id":"1902.08856","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5d33d3a13dffe497024ab29563051125bb0ef6ceee2ad39d7a94eb0943564e28","sha256:3cefa3c96a3b564ef4b062cfcda459335ba7c95d61ea86ea4d5092d23b1e39c7"],"state_sha256":"31073bc8e15fb4b7191ace3c68719acf45148755ac133a13caf88bd851b299a7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9H2ho8kKM4e7wvkXNivlSgV6biM3KybFoSMCLaBTlvmJSwi4NbnRKOMHXdkh2yAERVrytyWyEN5ckPU3p7DxDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T13:32:24.455658Z","bundle_sha256":"99f56316725530591a3ce32107c98c5e1e8ee3b5da6ed86531710e665cd6bd72"}}