{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:KMROTYX6PWOUOHCOEPSI2MQWMX","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":"7f9293191862e39fca0d0f1cf6af5b69d8f4fde1119da9c436840186bf0641f9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-30T22:10:52Z","title_canon_sha256":"9ad9500ba247baaa84b8b184cb4a57fe0d41147261f54e330185d2ae990269ea"},"schema_version":"1.0","source":{"id":"1909.00080","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.00080","created_at":"2026-07-05T00:04:29Z"},{"alias_kind":"arxiv_version","alias_value":"1909.00080v2","created_at":"2026-07-05T00:04:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.00080","created_at":"2026-07-05T00:04:29Z"},{"alias_kind":"pith_short_12","alias_value":"KMROTYX6PWOU","created_at":"2026-07-05T00:04:29Z"},{"alias_kind":"pith_short_16","alias_value":"KMROTYX6PWOUOHCO","created_at":"2026-07-05T00:04:29Z"},{"alias_kind":"pith_short_8","alias_value":"KMROTYX6","created_at":"2026-07-05T00:04:29Z"}],"graph_snapshots":[{"event_id":"sha256:c2df2d319a22c8bf2f83f8e35ac801e498971931f84c8b02d2adfb197a775dd6","target":"graph","created_at":"2026-07-05T00:04:29Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1909.00080/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Text classification has been one of the major problems in natural language processing. With the advent of deep learning, convolutional neural network (CNN) has been a popular solution to this task. However, CNNs which were first proposed for images, face many crucial challenges in the context of text processing, namely in their elementary blocks: convolution filters and max pooling. These challenges have largely been overlooked by the most existing CNN models proposed for text classification. In this paper, we present an experimental study on the fundamental blocks of CNNs in text categorizati","authors_text":"Avinash Madasu, Vijjini Anvesh Rao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-30T22:10:52Z","title":"Sequential Learning of Convolutional Features for Effective Text Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.00080","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:a46cb28a09177e54783f9e0fc305d3e86fc5ea4fc3c816f1e973ee72d74bcc6e","target":"record","created_at":"2026-07-05T00:04:29Z","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":"7f9293191862e39fca0d0f1cf6af5b69d8f4fde1119da9c436840186bf0641f9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-30T22:10:52Z","title_canon_sha256":"9ad9500ba247baaa84b8b184cb4a57fe0d41147261f54e330185d2ae990269ea"},"schema_version":"1.0","source":{"id":"1909.00080","kind":"arxiv","version":2}},"canonical_sha256":"5322e9e2fe7d9d471c4e23e48d321665dc278333578c1c7e1bc52a54adb4014b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5322e9e2fe7d9d471c4e23e48d321665dc278333578c1c7e1bc52a54adb4014b","first_computed_at":"2026-07-05T00:04:29.910311Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:04:29.910311Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WskMlRE5nrp5nBCbAqIADk9AodH24l/OIUy7YqrMbWmuhLtfU/HYuW5eFI+e5IrB9Ht2USBHStWAr8RAjd7UBg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:04:29.911261Z","signed_message":"canonical_sha256_bytes"},"source_id":"1909.00080","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a46cb28a09177e54783f9e0fc305d3e86fc5ea4fc3c816f1e973ee72d74bcc6e","sha256:c2df2d319a22c8bf2f83f8e35ac801e498971931f84c8b02d2adfb197a775dd6"],"state_sha256":"d3546f73c5abec0230498d3ffc58c7d4da5faf17909d4eea565fce6153c7eda7"}