{"as_of":"2026-08-16T15:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:12690f465082514c2fe8b7a6bd6e5b6051b82cacc268bfd57fb2a2a0af40d97a","coverage":[{"denominator":22,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":22,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T12:12:49.213341Z","state":"measured"},{"denominator":22,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":22,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/1908.07721/citation-record","integrity":"/paper/1908.07721/integrity","json":"/paper/1908.07721/citation-record.json","paper":"/paper/1908.07721"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:12:49.635117Z","title":"The emergence of national electronic health record architectures in the united states and australia: models, costs, and questions,","venue":null,"work_id":"1a23f403-8154-44a1-ba3c-25002a0cad86","year":2005},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.089425Z"},"links":{"citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:14b70747ac7ffb653b64ea06ac4f6088b9ac08a978ad79c5e5f9c3505e33e41e","observation_id":"1f56b774-e1f1-4e05-8263-53e6b26c597c","resolution":{"observed_at":"2026-08-14T12:12:49.640663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:12:49.619382Z","title":"Joint extraction of entities and relations based on a novel tagging scheme,","venue":null,"work_id":"18aa1129-abb6-4a08-bebe-0dc7124bf7d2","year":2017},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.095098Z"},"links":{"citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:ed20dd673cda0127123d5aa1375ded99b983a6c43132f6af52483d871ecbbc74","observation_id":"6037a79c-f77b-4fb1-9f3b-cbb2e731413a","resolution":{"observed_at":"2026-08-14T12:12:49.624907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:12:49.598768Z","title":"Joint type inference on entities and relations via graph convolutional networks,","venue":null,"work_id":"7db10db4-5b1e-4462-94e9-87a45ae87927","year":2019},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.100225Z"},"links":{"citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:345584addd1ed5869baa6749b63e1ff6edef4e83d945e39e056ff08e22e96498","observation_id":"3deff91f-ad78-4468-9231-93943ae56517","resolution":{"observed_at":"2026-08-14T12:12:49.606988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:12:49.582600Z","title":"Joint entity and relation extraction based on a hybrid neural network,","venue":null,"work_id":"f3b7cf27-edef-4e4f-a5e9-307ce04e6ca9","year":2017},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.105589Z"},"links":{"citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:2d18ce16b6332c54c06a954980f59df838f25d285fa4c7f8928c6e3ec4f7220d","observation_id":"6ba6797b-e03e-42b9-bc50-4729e6535413","resolution":{"observed_at":"2026-08-14T12:12:49.587578Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.10503","last_updated":"2019-04-23T19:18:26Z","snapshot_observed_at":"2026-08-14T16:42:30.184756Z","submitted_at":"2019-04-23T19:18:26Z","title":"Fine-Grained Named Entity Recognition using ELMo and Wikidata","version":1},"cited_work":{"arxiv_id":"1904.10503","doi":null,"metadata_source":"pith","pith_arxiv_id":"1904.10503","snapshot_observed_at":"2026-08-14T12:12:49.300471Z","title":"Fine-Grained Named Entity Recognition using ELMo and Wikidata","venue":"cs.IR","work_id":"63954e32-9cce-4206-99ed-758cd34c6a69","year":2019},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.110991Z"},"links":{"cited_paper":"/paper/1904.10503","citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:8ce399ea4066a9d9eba871d8ee58e6606f4a3b2d2b3260dfa503edea552046ca","observation_id":"0115e972-f516-4f63-bd8d-cb35052aa337","resolution":{"observed_at":"2026-08-14T12:12:49.306046Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.03088","last_updated":"2019-06-07T13:31:09Z","snapshot_observed_at":"2026-08-14T16:19:15.125004Z","submitted_at":"2019-06-07T13:31:09Z","title":"Improving Relation Extraction by Pre-trained Language Representations","version":1},"cited_work":{"arxiv_id":"1906.03088","doi":null,"metadata_source":"pith","pith_arxiv_id":"1906.03088","snapshot_observed_at":"2026-08-14T12:12:49.275530Z","title":"Improving Relation Extraction by Pre-trained Language Representations","venue":"cs.CL","work_id":"397ead74-8ead-4008-9375-99beb114a087","year":2019},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.117048Z"},"links":{"cited_paper":"/paper/1906.03088","citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:2e75edcda5b527f4026f04a32d398186cb4bb05933880b2502b93fc9d7807920","observation_id":"953339cc-9478-4998-9a9f-41e7c92f58fe","resolution":{"observed_at":"2026-08-14T12:12:49.282773Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:12:49.122961Z","title":"BERT: Pre- training of deep bidirectional transformers for language understanding,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.122961Z"},"links":{"citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:48769e3522b34d28609d70105eed713f9dc7f5f7c843497d5f039dac8435c0bf","observation_id":"fd8c2562-bfca-49f2-85c8-3ec893ccd321","resolution":{"observed_at":"2026-08-14T12:12:49.122961Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:12:49.552231Z","title":"Krishnan and V","venue":null,"work_id":"dd718885-7ea3-4799-b557-c6989d70c438","year":2005},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.128234Z"},"links":{"citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:4f76a4420b56f826512e555fcd50be14575e63a1fd9f7b2b54c3261662758ac5","observation_id":"a7830ba9-aef4-4fb0-9795-0a7483581eeb","resolution":{"observed_at":"2026-08-14T12:12:49.557755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:12:49.532592Z","title":"Automatic recognition of disorders, findings, pharmaceuticals and body structures from clinical text: An annotation and machine learning study,","venue":null,"work_id":"508e1928-ca72-475c-8d96-7cebbf9f6e27","year":2014},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.133321Z"},"links":{"citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:444e291dea8a656db2286cdf5a5bcc87e04cda2667657966168489149ec52a3e","observation_id":"515c574d-b35d-4379-85dc-fd6837cb37fd","resolution":{"observed_at":"2026-08-14T12:12:49.539548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:12:49.513242Z","title":"Incorporating dictionaries into deep neural networks for the chinese clinical named entity recognition,","venue":null,"work_id":"d9725254-42d1-4dbb-aca4-e5b9cd69cdf6","year":2019},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.137893Z"},"links":{"citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:1a1ac32326d8967de464bfda3da40db0e3d5acc4e8b4e6f8292e6672ece0b012","observation_id":"e0c80ac9-5387-40b2-a72c-df9626dd1419","resolution":{"observed_at":"2026-08-14T12:12:49.519969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:12:49.495717Z","title":"Utd: Classifying semantic relations by combining lexical and semantic resources,","venue":null,"work_id":"8825b715-271a-4bb6-891e-c5b33ff3f5e5","year":2010},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.142412Z"},"links":{"citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:b597617c420bb232befe83274c7ec824851eb03b36b4174f3ebd76e7ee28eae7","observation_id":"6b070d34-1c29-4167-bcb0-5f716faebd0d","resolution":{"observed_at":"2026-08-14T12:12:49.501434Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:12:49.476270Z","title":"Kernel methods for relation extraction,","venue":null,"work_id":"e7d044ca-8274-4a88-b75e-0d95648917b3","year":2003},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.147147Z"},"links":{"citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:fbda04f224a469bfcc82c8a2773dc7cb1c82cea8cb7375f2695443999420c077","observation_id":"98818fef-b947-4d78-9741-ffa9d376f4c7","resolution":{"observed_at":"2026-08-14T12:12:49.482911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:12:49.458535Z","title":"Automatic severity classification of coronary artery disease via recurrent capsule network,","venue":null,"work_id":"72f73bf8-e88d-4d7e-a8ee-76ea2ee283fc","year":2018},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.152095Z"},"links":{"citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:454391a17b028dfc024b78377bf719ec7aa9162cfe4da195f18ca79f3a82d483","observation_id":"2f92daa4-e413-4e72-ad3a-301c60b92acf","resolution":{"observed_at":"2026-08-14T12:12:49.463868Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:12:49.420997Z","title":"Incremental joint extraction of entity mentions and relations,","venue":null,"work_id":"2f9f81d2-92e0-4dd2-b0c0-6a747521f007","year":2014},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.165461Z"},"links":{"citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:cf857098b35bdb1d6fe67b46a901dc33ac2c6183e0e5252e86eccdd64d196448","observation_id":"71cc8fc4-c091-4b26-bc89-11187e8a2128","resolution":{"observed_at":"2026-08-14T12:12:49.426507Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:12:49.402761Z","title":"A neural joint model for entity and relation extraction from biomedical text,","venue":null,"work_id":"81de4ceb-45a9-43d9-a0d6-b407c05cfe6d","year":2017},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.174023Z"},"links":{"citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:df50ee58dcb2a56aa1f12d073e83c625933444cbaf28651ac67829f4086333ca","observation_id":"a61bb2ba-7330-4578-a4be-5e6bfe511261","resolution":{"observed_at":"2026-08-14T12:12:49.408745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:12:49.180130Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.180130Z"},"links":{"citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:037f2f88cb65969bae8ab39884c426f88c51a66bd548dd51aa65678e380023fc","observation_id":"c0d868dd-7a1c-48c6-9037-4e9f41c725cf","resolution":{"observed_at":"2026-08-14T12:12:49.180130Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1508.01991","last_updated":"2015-08-09T06:32:47Z","snapshot_observed_at":"2026-08-14T22:37:11.812000Z","submitted_at":"2015-08-09T06:32:47Z","title":"Bidirectional LSTM-CRF Models for Sequence Tagging","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1508.01991","snapshot_observed_at":"2026-08-14T12:12:49.185170Z","title":"Bidirectional lstm-crf models for sequence tagging,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.185170Z"},"links":{"cited_paper":"/paper/1508.01991","citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:3cf30184f70c521b400c4612cab30483c50ee6f6643d07e8b14d87b891c26ddd","observation_id":"d44a82ac-af59-4a09-9631-538a5290b8e1","resolution":{"observed_at":"2026-08-14T12:12:49.185170Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:12:49.374002Z","title":"A strategy on selecting performance metrics for classifier evaluation,","venue":null,"work_id":"492f7c93-4ba4-4404-ae2b-2648924cd276","year":2014},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.190564Z"},"links":{"citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:4f642d675e78e6db41167b88727c703cb8300634c6b775c4ea41691481273341","observation_id":"ca6dba32-3925-436e-bb38-15fe70807c50","resolution":{"observed_at":"2026-08-14T12:12:49.379701Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:12:49.356513Z","title":"Character-level neural network for biomedical named entity recognition,","venue":null,"work_id":"d6961709-447f-4361-b81d-ffa077d9eeec","year":2017},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.196359Z"},"links":{"citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:ee1c27fe25b91c14a9bc8308ca9a1061d390d1748669af71b2e9ef704a6f75b5","observation_id":"17e0f938-22d1-4a50-a1bc-456ce0d8e148","resolution":{"observed_at":"2026-08-14T12:12:49.362057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:12:49.338486Z","title":"Chinese clinical named entity recognition using residual dilated convolutional neural network with conditional random field,","venue":null,"work_id":"ba0aa3da-88df-472e-be9c-373a7f83167e","year":2019},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.202676Z"},"links":{"citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:ab8b2ef9974ec490db213a544de0d8c06d3b32c65341ecb72e17f1c879442761","observation_id":"2dc0c206-5dda-4569-b38b-049c7a582609","resolution":{"observed_at":"2026-08-14T12:12:49.344136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:12:49.438252Z","title":"Relation extraction from clinical texts using domain invariant convolutional neural network,","venue":null,"work_id":"3faee3b9-9a86-4d2b-84aa-7f803468abd7","year":2016},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.207888Z"},"links":{"citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:af408be34f858ee2fef134e325203874012c50dbc2fae51311f3905b0236cdbc","observation_id":"03d1f450-c4b6-478c-b5de-b449f1f1ed52","resolution":{"observed_at":"2026-08-14T12:12:49.443629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:12:49.317344Z","title":"Relation extraction: Perspective from convolutional neural networks,","venue":null,"work_id":"4c9ad57a-12bf-4fc1-809d-e5e11bc7840c","year":2015},"citing_paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T12:12:49.213341Z"},"links":{"citing_paper":"/paper/1908.07721"},"observation_digest":"sha256:4d87e24da67ca61e50565a77ff8e686f2d45a1002b85cea34243b18f8dde4375","observation_id":"67f55e05-5b02-434e-bc32-c56f1a4b3e33","resolution":{"observed_at":"2026-08-14T12:12:49.325686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.07721","last_updated":"2019-10-22T08:51:17Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-16T06:12:50.906023Z","submitted_at":"2019-08-21T06:56:08Z","title":"Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text"},"reference_resolution":{"displayed":22,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":2,"verified_fuzzy":17},"total_outbound_references":22},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:1908.07721."}