{"id":"W2889675133","doi":"10.18653/v1/d18-1121","title":"Put It Back: Entity Typing with Language Model Enhancement","year":2018,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Tsinghua University; National Natural Science Foundation of China; National Key Research and Development Program of China; China Association for Science and Technology","keywords":"Computer science; Natural language processing; Language model; Artificial intelligence; Benchmark (surveying); Entity linking; Context (archaeology); Code (set theory); Source code; Typing; Baseline (sea); Information retrieval; Programming language; Speech recognition","routes":{"ca_aff":true,"ca_fund":false,"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":[0.004196178,0.002387793,0.001557293,0.002134424,0.0009274213,0.002744919,0.00247435,0.001916376,0.007294303],"category_scores_gemma":[0.01177537,0.0008744364,0.002473555,0.002027262,0.0009018415,0.008927803,0.004360121,0.006091496,0.01277927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007721842,"about_ca_system_score_gemma":0.001527686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004059344,"about_ca_topic_score_gemma":0.009562709,"domain_scores_codex":[0.9967967,0.00131586,0.0001866795,0.0008701932,0.0006043487,0.0002262153],"domain_scores_gemma":[0.9925196,0.001989892,0.0002150335,0.003629348,0.001342354,0.0003038451],"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":[0.0005025246,0.0004264826,0.005859846,0.0005074566,0.0003800486,0.0004844407,0.0009150768,0.01895374,0.03022447,0.01793076,0.1247953,0.7990198],"study_design_scores_gemma":[0.0001325623,0.0002524671,0.00276295,0.0001589196,0.0003609541,0.001447571,0.0004744252,0.6871107,0.0665561,0.0756208,0.1648595,0.0002630829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02050929,0.002247747,0.9269727,0.005120608,0.00170666,0.0002060151,0.00227325,0.03452135,0.006442346],"genre_scores_gemma":[0.2365111,0.001483494,0.7150279,0.004607365,0.001343981,0.0003034313,0.01266137,0.004779338,0.02328207],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007294303,"threshold_uncertainty_score":0.02440184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02520113987968561,"score_gpt":0.264591339022488,"score_spread":0.2393901991428024,"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."}}