{"id":"W4236758004","doi":"10.1145/2766462.2767827","title":"Using Term Location Information to Enhance Probabilistic Information Retrieval","year":2015,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Probabilistic logic; Term (time); Divergence-from-randomness model; Term Discrimination; Information retrieval; Kernel (algebra); Artificial intelligence; Vector space model; Search engine; Concept search; Web search query; 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":[0.00325106,0.0009605314,0.001356076,0.002276662,0.0005899306,0.001239965,0.001379282,0.001177167,0.001686565],"category_scores_gemma":[0.01456511,0.0003329415,0.0008361625,0.002383776,0.0006737004,0.004292965,0.001199097,0.001082309,0.00148529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00124351,"about_ca_system_score_gemma":0.001302203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004610054,"about_ca_topic_score_gemma":0.007404441,"domain_scores_codex":[0.9982991,0.0006279896,0.0001682636,0.000333073,0.0004470367,0.0001246681],"domain_scores_gemma":[0.9945374,0.003063288,0.0005573151,0.0007142042,0.0009736185,0.0001541561],"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":[0.002416911,0.0009653262,0.01234111,0.000999173,0.000336908,0.0002920112,0.0004848677,0.143709,0.09127513,0.00887259,0.007556568,0.7307504],"study_design_scores_gemma":[0.0002056287,0.001396194,0.009356327,0.00005814262,0.0004137249,0.000535859,0.0001168777,0.9326001,0.03817743,0.01051267,0.006456559,0.0001703979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3141175,0.007525964,0.6647122,0.001305363,0.0002958504,0.000347666,0.0006274038,0.005209365,0.005858717],"genre_scores_gemma":[0.8626512,0.001222539,0.1315325,0.0002452781,0.0002112942,0.0001458488,0.000619228,0.0001675859,0.00320461],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004610054,"threshold_uncertainty_score":0.01719344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02434277484912716,"score_gpt":0.3131374087954922,"score_spread":0.288794633946365,"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."}}