{"id":"W2783530388","doi":"10.1111/coin.12152","title":"Improving text relatedness by incorporating phrase relatedness with word relatedness","year":2018,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"SemEval; Phrase; Computer science; Word (group theory); Natural language processing; Artificial intelligence; n-gram; Mathematics; Task (project management); Language model","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":[0.002959796,0.001862114,0.001126322,0.006419666,0.0009366076,0.001560529,0.001018317,0.001577771,0.002877316],"category_scores_gemma":[0.01407212,0.0003207808,0.001307911,0.003510901,0.000638184,0.00502653,0.002666065,0.001365055,0.00335255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006013434,"about_ca_system_score_gemma":0.0008405257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003591181,"about_ca_topic_score_gemma":0.005251914,"domain_scores_codex":[0.9959223,0.001697334,0.0002952193,0.0009900136,0.000864053,0.0002309633],"domain_scores_gemma":[0.9952003,0.002196483,0.0005798389,0.000673598,0.001103403,0.0002463912],"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":[0.001591791,0.00135462,0.06294082,0.001862326,0.0009536974,0.0006995095,0.001393116,0.059921,0.05223684,0.008026171,0.05658326,0.7524368],"study_design_scores_gemma":[0.0002106552,0.001862003,0.06028954,0.0003100938,0.0007697744,0.001164784,0.001105219,0.8261307,0.03416222,0.03694007,0.03679173,0.0002631785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5617146,0.01003202,0.3846934,0.001101841,0.0006481095,0.0006553603,0.007590707,0.01165104,0.02191301],"genre_scores_gemma":[0.8446369,0.0009711123,0.1320786,0.0004046145,0.0004060896,0.0002884073,0.01499087,0.0006088186,0.005614582],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.006419666,"threshold_uncertainty_score":0.01565313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02036011953862601,"score_gpt":0.2579900237378812,"score_spread":0.2376299041992551,"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."}}