{"as_of":"2026-08-16T09:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e8e8d248454abe38dfb79334da0aa35a29cfbb3171aad3f4582ed481157f36c4","coverage":[{"denominator":48,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T11:26:18.003558Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"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/1908.09128/citation-record","integrity":"/paper/1908.09128/integrity","json":"/paper/1908.09128/citation-record.json","paper":"/paper/1908.09128"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:26:18.710071Z","title":"End-to-end sequence labeling via bi-directional lstm-cnns-crf,","venue":null,"work_id":"4c57936d-ed70-47a9-927f-95aad383b027","year":2016},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.795755Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:eab1023b70ff5e8bae7a453063e4f6bed0f06a16ddb90924be33ab29e9b3465f","observation_id":"47e230bc-93f1-44d3-b7bd-01f0a043b062","resolution":{"observed_at":"2026-08-14T11:26:18.714488Z","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":"2026-08-14T11:26:18.695537Z","title":"Empower sequence labeling with task-aware neural language model,","venue":null,"work_id":"9992cc4e-9077-4bba-a59c-c76c1251065d","year":2018},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.801209Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:fe5cb9ad6b052e810d478e5bece2b38478f79de342afbafb13d000c547a323c0","observation_id":"8715ad67-e0fc-4624-a744-edb74fec66f6","resolution":{"observed_at":"2026-08-14T11:26:18.700442Z","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":"2026-08-14T11:26:18.681732Z","title":"Neural architectures for named entity recognition,","venue":null,"work_id":"1419972a-68a5-480e-9bb9-98165b409b6d","year":2016},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.806167Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:c62375e32bf1c6b663fa0872b966f6169d5c4bab92be592923079e46bf6da846","observation_id":"3a1a8cc6-d91e-4462-a155-f3aa3bd011cf","resolution":{"observed_at":"2026-08-14T11:26:18.686372Z","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":"2026-08-14T11:26:18.668564Z","title":"Design challenges and misconceptions in named entity recognition,","venue":null,"work_id":"19211789-217e-4d60-98c9-a9014d0b24c1","year":2009},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.811082Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:487e1008f440eb3e7e51e0ffe1b7f2e99ef619d7d5987a8b98b1ff39fb2fe640","observation_id":"d25c6f6f-d8fb-4971-b70b-9ce5a2ca6ec2","resolution":{"observed_at":"2026-08-14T11:26:18.672813Z","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":"2026-08-14T11:26:18.655570Z","title":"Lexicon infused phrase embeddings for named entity resolution,","venue":null,"work_id":"c97c9f4a-f101-4323-b81a-849dfe94e30c","year":2014},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.815766Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:a674e69f31d292b1826ad941df733f84807d3bf04961a5b2036e8bcc7d17cd5e","observation_id":"d1605f9f-6cd4-4bfa-83cd-cd89c18bba72","resolution":{"observed_at":"2026-08-14T11:26:18.659706Z","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":"2026-08-14T11:26:18.640909Z","title":"Deterministic dependency parsing of english text,","venue":null,"work_id":"5a018816-1e59-4eb0-aab1-75dafb4de487","year":2004},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.820664Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:9fb0694e9daee318371283ceb71b4ffadf31a324683614c1c685fe0a96bc87d9","observation_id":"e223486a-613f-4ca1-a7ef-e50b46e79ca7","resolution":{"observed_at":"2026-08-14T11:26:18.645829Z","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":"2026-08-14T11:26:18.624919Z","title":"Heterogeneous supervision for relation extraction: A representation learning approach,","venue":null,"work_id":"7216e9de-5d27-47a0-9d47-88d7e70c5456","year":2017},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.825958Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:ed9f35af0cf6022a873f8e3a632af4e4c890662e21884ad1ac6511dab52b84a6","observation_id":"7cc38820-0bb3-4539-9b70-e6f1a091674b","resolution":{"observed_at":"2026-08-14T11:26:18.629987Z","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":"2026-08-14T11:26:18.609008Z","title":"Supervised noun phrase coreference research: The first fifteen years,","venue":null,"work_id":"38866737-305d-4938-94fe-682721f08235","year":2010},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.830666Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:22722aab47f88773757403fe8cc181307e0f8315922a9ec04abcab9477a71e94","observation_id":"9e3b5dc8-7bd7-4fda-8f07-5caed608d7d5","resolution":{"observed_at":"2026-08-14T11:26:18.614533Z","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":"2026-08-14T11:26:18.592986Z","title":"Statistical inference for probabilistic functions of finite state markov chains,","venue":null,"work_id":"0f279a48-4a86-424c-ada1-d49dba30664b","year":1966},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.835231Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:1a4f6f44a03d9148138c14dffcb5767fcc8685a7fca5c2228e35698783639688","observation_id":"3011f1cd-0844-4fc9-bb24-1acc5b165146","resolution":{"observed_at":"2026-08-14T11:26:18.598303Z","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":"2026-08-14T11:26:18.577058Z","title":"Conditional random fields: Probabilistic models for segmenting and labeling sequence data,","venue":null,"work_id":"3b05366a-dcac-4449-8c42-e19ec3054c34","year":2001},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.839695Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:a0d509b60ca9f2ffe3d20d88e9ae6a7a8019aff20e85edf31f7d1264863edb8f","observation_id":"77f1d785-8ca8-495d-8948-0721d2aab2cb","resolution":{"observed_at":"2026-08-14T11:26:18.582112Z","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":"2026-08-14T11:26:18.560620Z","title":"Natural language processing (almost) from scratch,","venue":null,"work_id":"3a3c684f-ed28-4da8-b405-650d4a066bd0","year":2011},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.843983Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:83c6aabed5286a86bc74df74e6c1685b08bd7d14136b2fad2a4e07e171d9c9fe","observation_id":"084d1eb0-300a-4abf-a7dc-8e8872edba68","resolution":{"observed_at":"2026-08-14T11:26:18.566069Z","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":"2026-08-14T11:26:18.544470Z","title":"Named entity recognition with bidirectional lstm-cnns,","venue":null,"work_id":"dc1abc22-4018-4814-8835-c30825ff938b","year":2016},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.848648Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:356ecce4a32d68a9768e23bcd5c705111fec563b20e4064440d13efc137a8ea3","observation_id":"55a64ac8-3a89-4587-9a88-85e65a2f5541","resolution":{"observed_at":"2026-08-14T11:26:18.549330Z","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":"2026-08-14T11:26:18.530517Z","title":"Deep semantic role labeling with self-attention,","venue":null,"work_id":"4809ad17-d345-4845-bae4-b3210998f2f1","year":2018},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.852727Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:5e14bcadef59724aebdb92096211120f98dae91fcc9de644ee9b009ebda6ec70","observation_id":"b4c945f4-0439-423d-96c9-cc7cecec2db2","resolution":{"observed_at":"2026-08-14T11:26:18.534830Z","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":"2026-08-14T11:26:18.516778Z","title":"Long short-term memory-networks for machine reading,","venue":null,"work_id":"c08fd9ec-f173-4b87-bb5f-40536f492faa","year":2016},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.856729Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:3a96c207697f661def3b0acc439dcabcdbd0435dfbcc0d2c1f8056c81b314863","observation_id":"46e47336-f62e-4c93-b432-fbee68d6e634","resolution":{"observed_at":"2026-08-14T11:26:18.521401Z","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":"2026-08-14T11:26:18.503688Z","title":"Attention is all you need,","venue":null,"work_id":"7da72263-6b90-4445-a1c5-11d3ee7ac0fe","year":2017},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.860583Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:7b2b686854977ae23e325cfbae72e46affe394fb21be15728410adea91f8e78b","observation_id":"5e0ae4b8-600c-4297-a173-16886ddac407","resolution":{"observed_at":"2026-08-14T11:26:18.508039Z","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":"2026-08-14T11:26:18.490996Z","title":"A structured self-attentive sentence embedding,","venue":null,"work_id":"179c1f24-ac09-4c05-995b-d00baaa4942a","year":2017},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.864260Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:7b5b45225d79476cd2a3656d4c4a61753582d2f51fd123e7e6dac35f8548ca1f","observation_id":"708b6989-8141-457a-9e06-b0f910903d0c","resolution":{"observed_at":"2026-08-14T11:26:18.495005Z","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":"2026-08-14T11:26:17.868138Z","title":"Self-attention with relative position representations,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.868138Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:9cfad8371c7c97c8686d4032c0569c9acee87f72802214897b0c96e17e950ccf","observation_id":"04610b60-71f7-4241-977f-eba7eed24353","resolution":{"observed_at":"2026-08-14T11:26:17.868138Z","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":"2026-08-14T11:26:18.469002Z","title":"A tutorial on hidden markov models and selected applications in speech recognition,","venue":null,"work_id":"909a20c0-bce1-4f80-b040-ec63048fc2b0","year":1989},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.871929Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:0a3d79f9b0fa085fbe76bc644b02bdad8a1f61843a8ec28ea2224e32616e185b","observation_id":"e7f7bca8-a8c1-42c8-9b6c-c1fff0849e55","resolution":{"observed_at":"2026-08-14T11:26:18.473419Z","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":"2026-08-14T11:26:18.455102Z","title":"Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons,","venue":null,"work_id":"1e3c3f72-31ab-4453-abd3-91e64a9f7d27","year":2003},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.875482Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:49abe0fae6a8ecbe96bb50a5c06701ae8b241cdcf92504bf652eff7a93dd286e","observation_id":"43adde5c-1fa4-4053-8160-4da70845b7f9","resolution":{"observed_at":"2026-08-14T11:26:18.459714Z","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":"2026-08-14T11:26:18.441638Z","title":"Use of support vector learning for chunk identification,","venue":null,"work_id":"07c6177f-53b9-44c8-85b4-1be76cd56ed7","year":2000},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.879179Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:442bad6401ce5809af4390015e5482b44eaba6a57dd751933e24cf3dfabc849f","observation_id":"4ba57bfa-66c3-4bf9-9722-0ea4b54bd24a","resolution":{"observed_at":"2026-08-14T11:26:18.446131Z","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":"2026-08-14T11:26:18.428203Z","title":"Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms,","venue":null,"work_id":"8c68304a-989f-47a6-9432-59dfcdcfc4ad","year":2002},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.883823Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:d4242ad86d3bf3e958659e8f3fb951ce1c7ca828ddea93768610e17208424072","observation_id":"1682b767-9de8-457f-a4b1-67e9add0efa7","resolution":{"observed_at":"2026-08-14T11:26:18.432632Z","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":"2026-08-14T11:26:18.414977Z","title":"Bidirectional lstm-crf models for sequence tagging,","venue":null,"work_id":"2b478db1-07f9-48e4-9751-136d45c1c34e","year":2015},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.888120Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:5eea8dcf26cc847c3a0a008d5337a5152498110662c655d31ab362efb70a3a6d","observation_id":"21fd9f44-97c0-4e6e-a9d5-ff113a900688","resolution":{"observed_at":"2026-08-14T11:26:18.419383Z","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":"2026-08-14T11:26:18.400246Z","title":"Learning tag dependencies for sequence tagging","venue":null,"work_id":"8e419ade-472c-4f94-ba17-54bf07e0846e","year":2018},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.892295Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:140a5a46478d6273b50d8b110e73865906e2023f7b9c32a2e41a0aa087efe546","observation_id":"66308d83-293f-41c4-b91b-b265b0e11f92","resolution":{"observed_at":"2026-08-14T11:26:18.405406Z","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":"2026-08-14T11:26:18.385755Z","title":"Transfer learning for sequence tagging with hierarchical recurrent networks,","venue":null,"work_id":"6558f984-e139-44ca-8c20-f7b68538e1b0","year":2017},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.896476Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:dc67eabe1ba95bea41f145f1cb019a35f133ecd64f0385e20ac9a7039c63b7b5","observation_id":"d63916a0-afff-4769-890b-aedd7f6aa843","resolution":{"observed_at":"2026-08-14T11:26:18.390270Z","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":"2026-08-14T11:26:18.372060Z","title":"Segbot: A generic neural text segmentation model with pointer network","venue":null,"work_id":"9505adcc-8884-418b-86ed-4026c7a79512","year":2018},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.900902Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:7ac9efbb3fe59630c47fb75898509cfe1e3bbe616acc8f0de0c143590100cff4","observation_id":"731fcd0b-7dbd-4cfa-a0f3-b141626ed18e","resolution":{"observed_at":"2026-08-14T11:26:18.376817Z","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":"2026-08-14T11:26:18.357973Z","title":"Disan: Direc- tional self-attention network for rnn/cnn-free language understanding,","venue":null,"work_id":"dc032b45-7011-4366-b4ac-3b493308628f","year":2018},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.905382Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:81b38ab2c4005f6fe813c91bd835ef98f88438d09533ddd19ff82b21a763d6d2","observation_id":"6ad9937d-c850-4255-bccb-6d4778d51475","resolution":{"observed_at":"2026-08-14T11:26:18.362759Z","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":"2026-08-14T11:26:18.343557Z","title":"Self- attentional acoustic models,","venue":null,"work_id":"c1677c18-1f9f-4283-bef3-d111e983c675","year":2018},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.909967Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:1796f15b101a5332adb99cbb3da30a0d725fc8d5456067464376be137825bb75","observation_id":"b417c729-2bbb-4dcb-a83c-7188fc71c4c1","resolution":{"observed_at":"2026-08-14T11:26:18.348670Z","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":"2026-08-14T11:26:18.330306Z","title":"Learning character-level representa- tions for part-of-speech tagging,","venue":null,"work_id":"10afb94c-fbe4-49cc-8185-7537c28bb0b2","year":2014},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.914490Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:f263262641974141362c3ea23565424417ccfb3f8cce42b80f52162969db551a","observation_id":"afc9543c-f26f-4bab-bc91-0ae24a3d6225","resolution":{"observed_at":"2026-08-14T11:26:18.334817Z","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":"2026-08-14T11:26:17.919085Z","title":"Long short-term memory,","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.919085Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:ae574c8e50cc223a9da22074d1f15112ab596180ca61788af46c23e69a99449c","observation_id":"76addf34-8cc2-457f-914d-4a11e3b386e9","resolution":{"observed_at":"2026-08-14T11:26:17.919085Z","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":"2026-08-14T11:26:18.307933Z","title":"Neural machine translation by jointly learning to align and translate,","venue":null,"work_id":"d5a31c4e-32e1-4812-9a44-c85922de9260","year":2014},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.923841Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:b8d10fd5ae5b2210bd587aedfe4e0a0d9425f37f1d9ac2914e16d8c1e5d0fc44","observation_id":"a724b768-279a-46c7-9ebf-baca31c0c6af","resolution":{"observed_at":"2026-08-14T11:26:18.312433Z","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":"2026-08-14T11:26:18.293501Z","title":"Effective approaches to attention-based neural machine translation,","venue":null,"work_id":"110c7f0e-61a4-4968-99c9-3fb737a728a4","year":2015},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.928502Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:eb001b98e38f45aeaca679e9dd98d70584a31541598865272f49c1cb8249cd09","observation_id":"360295d6-98b6-4186-8e59-2678ea7de069","resolution":{"observed_at":"2026-08-14T11:26:18.298778Z","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":"2026-08-14T11:26:18.279278Z","title":"Semi- supervised sequence tagging with bidirectional language models,","venue":null,"work_id":"87969223-da94-4344-8a66-fc61e773aff8","year":2017},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.932858Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:a9d1d9f1a1f79c13212d46b3610901c67aafdff3678089c0f8bca2b07d0bb586","observation_id":"57b8c177-67cd-4eee-b99a-b7d97f822559","resolution":{"observed_at":"2026-08-14T11:26:18.283777Z","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":"2026-08-14T11:26:18.265286Z","title":"Glove: Global vectors for word representation,","venue":null,"work_id":"43b43d0b-8152-4bc2-bd27-2fdc3644c988","year":2014},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.937304Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:519a879ec6975510a195a37064e3daba630854584a97bec3ffa59b8191ea6f0b","observation_id":"7a8d27fb-877a-4c28-b9f1-201cf4cf1720","resolution":{"observed_at":"2026-08-14T11:26:18.269911Z","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":"2026-08-14T11:26:17.941615Z","title":"Understanding the difficulty of training deep feedforward neural networks,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.941615Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:9166b7c9c6aa2a25ab3e4dd1dd55f1681cbeb13f9558b4100221fed91b43a11b","observation_id":"c51651fe-402d-4f81-a038-7fddc4496264","resolution":{"observed_at":"2026-08-14T11:26:17.941615Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:26:17.945785Z","title":"On the difficulty of training recurrent neural networks,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.945785Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:504024c6b010c669e60d3f90d41dafffc8c0aaf90fd7f230637732b1c09c9fa7","observation_id":"635cbba5-8b99-486d-8b7d-c065945acf71","resolution":{"observed_at":"2026-08-14T11:26:17.945785Z","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":"2026-08-14T11:26:18.232226Z","title":"Over- fitting in neural nets: Backpropagation, conjugate gradient, and early stopping,","venue":null,"work_id":"d6f14cb8-cc0d-417d-a98c-f84a419c8359","year":2000},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.950486Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:173da6e1ff0f581baccf0117eba5412616eef249fdc549e8cc8274aa70ba0246","observation_id":"bb2092de-be42-45b7-9456-9740d609fd2d","resolution":{"observed_at":"2026-08-14T11:26:18.236898Z","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":"2026-08-14T11:26:18.218795Z","title":"Semi-supervised multitask learning for sequence labeling,","venue":null,"work_id":"20e20d32-eda6-4da5-8379-acc68caff1b8","year":2017},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.954869Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:fdcc008de0be8f1735f806fe5fa8d2037f8fda0010d1f7b5643e69e9c0ae29c9","observation_id":"63501c68-205c-4092-8fcc-5611d8b0f30a","resolution":{"observed_at":"2026-08-14T11:26:18.222926Z","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":"2026-08-14T11:26:18.205173Z","title":"Does higher order lstm have better accuracy for segmenting and labeling sequence data?","venue":null,"work_id":"d236eb9e-0d86-44dc-8822-4010ecebf4da","year":2017},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.959182Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:ab9184088e3f52bd3fa113abad719da25b1eb65c5162c4a60e46507ed4513d3b","observation_id":"c3ea0220-2f83-4b18-899b-06f90f6505a9","resolution":{"observed_at":"2026-08-14T11:26:18.209760Z","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":"2026-08-14T11:26:18.190819Z","title":"Deep contextualized word representations,","venue":null,"work_id":"40f72cd0-55a7-448f-a14e-532a2a3ac322","year":2018},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.963568Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:41014747a87cb726bc1fa6e26d95260d4f3e292d490df5ac84809aaccb008c44","observation_id":"019cde1b-1979-4b65-9faf-e94acba9b03f","resolution":{"observed_at":"2026-08-14T11:26:18.195316Z","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":"2026-08-14T11:26:18.176854Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding,","venue":null,"work_id":"adbd96ed-f9c3-407d-9c5b-0cea8380194a","year":2018},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.967873Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:520478dfc08990eda8491f08223c9ba73622f2b6bbb8cbf20b27ee220d72196c","observation_id":"46bacd64-925d-4a95-98ee-cde06f163450","resolution":{"observed_at":"2026-08-14T11:26:18.181630Z","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":"2026-08-14T11:26:18.160712Z","title":"Contextual string embeddings for sequence labeling,","venue":null,"work_id":"6aee7b1b-3ae9-4c0f-9b9f-0eb2cdb79fa3","year":2018},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.972168Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:7f6692e968b178153d6ef8530c6d855e9a014057c88bfdb97b1259a74ec54d8e","observation_id":"d5cd4342-28d8-46b9-9240-0ae802487b69","resolution":{"observed_at":"2026-08-14T11:26:18.166032Z","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":"2026-08-14T11:26:18.144199Z","title":"Guided learning for bidirectional sequence classification,","venue":null,"work_id":"eb892884-2259-4908-ab14-9a7bded4fbb2","year":2007},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.976499Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:6e62ef83b45bbeb615e88a38b15cb13fbee29223430e960c0cb4d6ccaca07660","observation_id":"8017a01a-a6e6-4f8f-b97f-8aebbd9cdfe3","resolution":{"observed_at":"2026-08-14T11:26:18.149288Z","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":"2026-08-14T11:26:18.126359Z","title":"Structure regularization for structured prediction,","venue":null,"work_id":"014f86a6-028d-4907-a746-67e63cc2d150","year":2014},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.980762Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:c806f50871d2033d4b09bd458874578a68b1cb80e8d6f5d5b35592c6448552d0","observation_id":"54fd53a8-fda7-49be-99f0-1e258150fe37","resolution":{"observed_at":"2026-08-14T11:26:18.132073Z","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":"2026-08-14T11:26:18.110739Z","title":"Semisupervised condensed nearest neighbor for part-of- speech tagging,","venue":null,"work_id":"29782aff-7e25-4115-bcaf-52db29d80d7c","year":2011},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.985374Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:557493ac5f058c604621b676f725d16d6320bd61c9f9a0e520a6dc0c30880ada","observation_id":"388ff793-cd32-4589-b9c8-9c357ff0b91f","resolution":{"observed_at":"2026-08-14T11:26:18.115783Z","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":"2026-08-14T11:26:18.095531Z","title":"Robust multilingual part-of- speech tagging via adversarial training,","venue":null,"work_id":"2d8ff531-e932-44d4-aac2-28327e824fd6","year":2017},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.989625Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:f6fc92a479de19d3b80ab0999a1315593ba8ed897c29880ac50a643c6fe19c17","observation_id":"48188212-2da3-4e99-bfed-1830e565907c","resolution":{"observed_at":"2026-08-14T11:26:18.100357Z","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":"2026-08-14T11:26:18.078959Z","title":"Feature-frequency–adaptive on-line training for fast and accurate natural language processing,","venue":null,"work_id":"d9173a98-3712-49b4-9f2f-13dcab2e6bd0","year":2014},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.994104Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:daa42b9c93735f734fc2737623590ad4cd418c642c1876d9d033b065b7adec72","observation_id":"c4e51d02-7357-4f13-a6ab-2cff0aebff30","resolution":{"observed_at":"2026-08-14T11:26:18.084627Z","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":"1611.04233","last_updated":"2016-11-14T02:48:46Z","snapshot_observed_at":"2026-08-14T21:30:38.118442Z","submitted_at":"2016-11-14T02:48:46Z","title":"A New Recurrent Neural CRF for Learning Non-linear Edge Features","version":1},"cited_work":{"arxiv_id":"1611.04233","doi":null,"metadata_source":"pith","pith_arxiv_id":"1611.04233","snapshot_observed_at":"2026-08-14T11:26:18.040522Z","title":"A New Recurrent Neural CRF for Learning Non-linear Edge Features","venue":"cs.CL","work_id":"7442314b-5f21-430f-9be4-9baa9b3b7e76","year":2016},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:17.998741Z"},"links":{"cited_paper":"/paper/1611.04233","citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:587a81e27d3920858435fbd45a7c90d0b1bdc9aa35e483b4456638b2b6cae2a6","observation_id":"563893ac-cf4c-4fda-b9ed-cc5790abee4e","resolution":{"observed_at":"2026-08-14T11:26:18.047250Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:26:18.062903Z","title":"Neural models for sequence chunking,","venue":null,"work_id":"91b87781-ef54-4156-9ab5-f4f03a3152d4","year":2017},"citing_paper":{"arxiv_id":"1908.09128","last_updated":"2021-10-16T13:11:22Z","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-14T11:26:18.003558Z"},"links":{"citing_paper":"/paper/1908.09128"},"observation_digest":"sha256:5c65eb4e6d90fdc2ccfacc56d66f7056adb5b94c8ed55f7cbb51ce7e7727ced5","observation_id":"729ca953-146b-4c75-8553-d56dab04c15b","resolution":{"observed_at":"2026-08-14T11:26:18.068035Z","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":"1908.09128","last_updated":"2021-10-16T13:11:22Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-14T11:18:50.668694Z","submitted_at":"2019-08-24T11:40:08Z","title":"Position-Aware Self-Attention based Neural Sequence Labeling"},"reference_resolution":{"displayed":48,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":1,"verified_fuzzy":43},"total_outbound_references":48},"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 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:1908.09128."}