{"id":"W2017541092","doi":"10.1145/1090785.1090809","title":"Semantic knowledge in word completion","year":2005,"lang":"en","type":"article","venue":"","topic":"Text Readability and Simplification","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Natural language processing; Artificial intelligence; Context (archaeology); Semantic similarity; Noun; Semantic compression; Word (group theory); Knowledge base; Semantic computing; Explicit semantic analysis; Semantics (computer science); Rank (graph theory); Information retrieval; Semantic Web; Semantic technology; Linguistics; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002014309,0.000042575,0.00005950514,0.00006440534,0.00002779557,0.00004100361,0.0002670441,0.00002472749,0.00003998158],"category_scores_gemma":[0.000009860139,0.00003822808,0.00001706503,0.0002522817,0.00001370847,0.000281387,0.00005189629,0.00004920724,0.0005058198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004661835,"about_ca_system_score_gemma":0.00001786642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003627637,"about_ca_topic_score_gemma":0.0007646286,"domain_scores_codex":[0.9994997,0.00003445292,0.0001269616,0.000167501,0.00006385886,0.0001075472],"domain_scores_gemma":[0.9996167,0.00004472195,0.00001559477,0.0002749613,0.00002175386,0.00002630161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000001656723,0.0002280987,0.00453197,0.000009472037,0.000001242332,5.270181e-7,0.001110172,0.0001459678,0.0008724048,0.4317343,0.0009378755,0.5604263],"study_design_scores_gemma":[0.0002877438,0.00002141927,0.5507277,0.00001294921,9.708235e-7,0.000007002038,0.00004340247,0.3840118,0.001602753,0.008356005,0.05475505,0.0001731733],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2439152,0.00005768184,0.7315438,0.004103417,0.00007570125,0.0001054933,1.30348e-7,0.0001566185,0.02004187],"genre_scores_gemma":[0.9774354,0.000002613196,0.02186496,0.000131053,0.00002925106,0.000005105087,0.000001006097,0.000001432391,0.0005291509],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7335202,"threshold_uncertainty_score":0.6501459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02680282494524568,"score_gpt":0.2757580693432861,"score_spread":0.2489552443980404,"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."}}