{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002030106,0.001363366,0.001300819,0.003029739,0.001013873,0.001729295,0.002430464,0.001285462,0.005344498],"category_scores_gemma":[0.01198977,0.0007042613,0.001589859,0.002265571,0.001715308,0.006817285,0.00290344,0.001511333,0.002108344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008172079,"about_ca_system_score_gemma":0.001755447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007496139,"about_ca_topic_score_gemma":0.007268848,"domain_scores_codex":[0.9978163,0.0006624932,0.0001143606,0.0006199053,0.0006324171,0.0001545436],"domain_scores_gemma":[0.9950284,0.002555623,0.0003588017,0.0009472364,0.0008785311,0.0002313934],"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":[0.0006663156,0.0005799842,0.005148423,0.0004920657,0.0001599867,0.0005033055,0.002748819,0.07706454,0.01505843,0.03304213,0.004340256,0.8601957],"study_design_scores_gemma":[0.00006998541,0.0002821607,0.003254155,0.00007583092,0.0001318924,0.0005893877,0.0007533213,0.7920305,0.01910293,0.172116,0.01147188,0.0001221032],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04329012,0.0003438258,0.9493874,0.000298335,0.00003168116,0.000163635,0.0001986875,0.003328842,0.002957347],"genre_scores_gemma":[0.4776379,0.000423259,0.5155984,0.0001675441,0.00009080231,0.0002972104,0.001206135,0.0003581458,0.004220574],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007496139,"threshold_uncertainty_score":0.01787919,"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."}}