{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:5BL6RUW2NFGQ7QCEIVWNTNRX2T","short_pith_number":"pith:5BL6RUW2","canonical_record":{"source":{"id":"2005.09207","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-05-19T04:18:04Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"018e3fee6fc14ba374584e3a1ea95ce272c7954c76c222c524d4c3ac055d522f","abstract_canon_sha256":"395c359a112600b2d1386e5021749703b58bad652b8f0e041fb0c5506daec685"},"schema_version":"1.0"},"canonical_sha256":"e857e8d2da694d0fc044456cd9b637d4d5b425a037ca0e8de4f9455a9f199b43","source":{"kind":"arxiv","id":"2005.09207","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2005.09207","created_at":"2026-07-05T01:06:11Z"},{"alias_kind":"arxiv_version","alias_value":"2005.09207v2","created_at":"2026-07-05T01:06:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.09207","created_at":"2026-07-05T01:06:11Z"},{"alias_kind":"pith_short_12","alias_value":"5BL6RUW2NFGQ","created_at":"2026-07-05T01:06:11Z"},{"alias_kind":"pith_short_16","alias_value":"5BL6RUW2NFGQ7QCE","created_at":"2026-07-05T01:06:11Z"},{"alias_kind":"pith_short_8","alias_value":"5BL6RUW2","created_at":"2026-07-05T01:06:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:5BL6RUW2NFGQ7QCEIVWNTNRX2T","target":"record","payload":{"canonical_record":{"source":{"id":"2005.09207","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-05-19T04:18:04Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"018e3fee6fc14ba374584e3a1ea95ce272c7954c76c222c524d4c3ac055d522f","abstract_canon_sha256":"395c359a112600b2d1386e5021749703b58bad652b8f0e041fb0c5506daec685"},"schema_version":"1.0"},"canonical_sha256":"e857e8d2da694d0fc044456cd9b637d4d5b425a037ca0e8de4f9455a9f199b43","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:06:11.315444Z","signature_b64":"0hoV4fe6H+ZLvtjBwl7aDGp6rfVz3JYZ+08U7L2ONgh53TL1xAHwd+PkFkpgbGjQ9PM0owqGMDToZ4cNTv8vAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e857e8d2da694d0fc044456cd9b637d4d5b425a037ca0e8de4f9455a9f199b43","last_reissued_at":"2026-07-05T01:06:11.315036Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:06:11.315036Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2005.09207","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":"2026-07-05T01:06:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WlJKlcUrffUpZTfL0S20Ws0DdRMVJnsNjbjFxdP19WXzh92WQCeqjEil3yH0C/PlkbvhqXfbv/3NDTqVoAkkBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T22:16:06.617531Z"},"content_sha256":"40a7819632077c1171acd095edf510fa76389b0d7a98c9ca066b2f5b63f4aac1","schema_version":"1.0","event_id":"sha256:40a7819632077c1171acd095edf510fa76389b0d7a98c9ca066b2f5b63f4aac1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:5BL6RUW2NFGQ7QCEIVWNTNRX2T","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Table Search Using a Deep Contextualized Language Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.IR","authors_text":"Brian D. Davison, Jeff Heflin, Mohamed Trabelsi, Yinan Xu, Zhiyu Chen","submitted_at":"2020-05-19T04:18:04Z","abstract_excerpt":"Pretrained contextualized language models such as BERT have achieved impressive results on various natural language processing benchmarks. Benefiting from multiple pretraining tasks and large scale training corpora, pretrained models can capture complex syntactic word relations. In this paper, we use the deep contextualized language model BERT for the task of ad hoc table retrieval. We investigate how to encode table content considering the table structure and input length limit of BERT. We also propose an approach that incorporates features from prior literature on table retrieval and jointly"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.09207","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2005.09207/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T01:06:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HnJ7XQ3pLh/rgzR7tyKW7wcC5PtqaHQCEbIiwuQ+BNWmSDI3Y6qnOu0YWt8mvRvyZ0YH7a26FcpObXWXTK2QDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T22:16:06.617959Z"},"content_sha256":"8cbd8fb39978e8f02d26347bcafd244a3e0bbc236821a55efdd9941ce9421614","schema_version":"1.0","event_id":"sha256:8cbd8fb39978e8f02d26347bcafd244a3e0bbc236821a55efdd9941ce9421614"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5BL6RUW2NFGQ7QCEIVWNTNRX2T/bundle.json","state_url":"https://pith.science/pith/5BL6RUW2NFGQ7QCEIVWNTNRX2T/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5BL6RUW2NFGQ7QCEIVWNTNRX2T/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":"2026-08-19T22:16:06Z","links":{"resolver":"https://pith.science/pith/5BL6RUW2NFGQ7QCEIVWNTNRX2T","bundle":"https://pith.science/pith/5BL6RUW2NFGQ7QCEIVWNTNRX2T/bundle.json","state":"https://pith.science/pith/5BL6RUW2NFGQ7QCEIVWNTNRX2T/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5BL6RUW2NFGQ7QCEIVWNTNRX2T/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:5BL6RUW2NFGQ7QCEIVWNTNRX2T","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":"395c359a112600b2d1386e5021749703b58bad652b8f0e041fb0c5506daec685","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-05-19T04:18:04Z","title_canon_sha256":"018e3fee6fc14ba374584e3a1ea95ce272c7954c76c222c524d4c3ac055d522f"},"schema_version":"1.0","source":{"id":"2005.09207","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2005.09207","created_at":"2026-07-05T01:06:11Z"},{"alias_kind":"arxiv_version","alias_value":"2005.09207v2","created_at":"2026-07-05T01:06:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.09207","created_at":"2026-07-05T01:06:11Z"},{"alias_kind":"pith_short_12","alias_value":"5BL6RUW2NFGQ","created_at":"2026-07-05T01:06:11Z"},{"alias_kind":"pith_short_16","alias_value":"5BL6RUW2NFGQ7QCE","created_at":"2026-07-05T01:06:11Z"},{"alias_kind":"pith_short_8","alias_value":"5BL6RUW2","created_at":"2026-07-05T01:06:11Z"}],"graph_snapshots":[{"event_id":"sha256:8cbd8fb39978e8f02d26347bcafd244a3e0bbc236821a55efdd9941ce9421614","target":"graph","created_at":"2026-07-05T01:06:11Z","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/2005.09207/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Pretrained contextualized language models such as BERT have achieved impressive results on various natural language processing benchmarks. Benefiting from multiple pretraining tasks and large scale training corpora, pretrained models can capture complex syntactic word relations. In this paper, we use the deep contextualized language model BERT for the task of ad hoc table retrieval. We investigate how to encode table content considering the table structure and input length limit of BERT. We also propose an approach that incorporates features from prior literature on table retrieval and jointly","authors_text":"Brian D. Davison, Jeff Heflin, Mohamed Trabelsi, Yinan Xu, Zhiyu Chen","cross_cats":["cs.CL","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-05-19T04:18:04Z","title":"Table Search Using a Deep Contextualized Language Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.09207","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":"sha256:40a7819632077c1171acd095edf510fa76389b0d7a98c9ca066b2f5b63f4aac1","target":"record","created_at":"2026-07-05T01:06:11Z","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":"395c359a112600b2d1386e5021749703b58bad652b8f0e041fb0c5506daec685","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-05-19T04:18:04Z","title_canon_sha256":"018e3fee6fc14ba374584e3a1ea95ce272c7954c76c222c524d4c3ac055d522f"},"schema_version":"1.0","source":{"id":"2005.09207","kind":"arxiv","version":2}},"canonical_sha256":"e857e8d2da694d0fc044456cd9b637d4d5b425a037ca0e8de4f9455a9f199b43","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e857e8d2da694d0fc044456cd9b637d4d5b425a037ca0e8de4f9455a9f199b43","first_computed_at":"2026-07-05T01:06:11.315036Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:06:11.315036Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0hoV4fe6H+ZLvtjBwl7aDGp6rfVz3JYZ+08U7L2ONgh53TL1xAHwd+PkFkpgbGjQ9PM0owqGMDToZ4cNTv8vAw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:06:11.315444Z","signed_message":"canonical_sha256_bytes"},"source_id":"2005.09207","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:40a7819632077c1171acd095edf510fa76389b0d7a98c9ca066b2f5b63f4aac1","sha256:8cbd8fb39978e8f02d26347bcafd244a3e0bbc236821a55efdd9941ce9421614"],"state_sha256":"fcbb2f5153d344fa97a3ddfef6d90c4e84ba0168a818594cdd071ab33c93846c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"evEZ29MbZvbSKwhs7wF31T63mZswXKxNc4tJPPUfEzKn6iKkSsPgMk7+njPEHD8Z4no933nWeXoLR0KAOPQgBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T22:16:06.620700Z","bundle_sha256":"46a075d7202400adea86470a14af8f17c95e4f7952453605aa70820fee920a53"}}