{"id":"W2899463504","doi":"10.18653/v1/w18-5618","title":"In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition","year":2018,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de la Défense Nationale; Genome British Columbia; Genome Canada","keywords":"Computer science; Named-entity recognition; Pipeline (software); Security token; Entity linking; Natural language processing; Domain (mathematical analysis); Artificial intelligence; Context (archaeology); Biomedical text mining; Named entity; Task (project management); Natural language; Information retrieval; Text mining; Knowledge base; Programming language","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001482967,0.00113325,0.0007163317,0.001811433,0.000446139,0.001319219,0.001058307,0.001305509,0.003372743],"category_scores_gemma":[0.006287613,0.0003284512,0.0009027975,0.001986279,0.0003440862,0.005336463,0.001583842,0.001960643,0.003422463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006094815,"about_ca_system_score_gemma":0.000999495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00347422,"about_ca_topic_score_gemma":0.006855193,"domain_scores_codex":[0.9990492,0.0003215897,0.00009801219,0.0003239084,0.0001054928,0.0001017689],"domain_scores_gemma":[0.9969153,0.001542443,0.000267277,0.0007417476,0.0004095975,0.0001235681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007072108,0.0007654545,0.01317798,0.0004849543,0.000309233,0.0003441088,0.0004431379,0.07330257,0.01891169,0.007857054,0.03769725,0.8459994],"study_design_scores_gemma":[0.00005388138,0.0001957015,0.003262239,0.00006043597,0.0001517682,0.0002490694,0.0002306622,0.947427,0.01820001,0.01601156,0.01409772,0.00005993346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2775572,0.005113631,0.6673477,0.002373665,0.001077677,0.00019299,0.008474329,0.03010903,0.007753755],"genre_scores_gemma":[0.7191991,0.001200982,0.2514061,0.0005669306,0.0003066708,0.0001273269,0.01929209,0.0006989377,0.007201763],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00347422,"threshold_uncertainty_score":0.01128292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01883654162191673,"score_gpt":0.2648734316090031,"score_spread":0.2460368899870863,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}