{"id":"W2468590752","doi":"10.18653/v1/s16-1118","title":"DalGTM at SemEval-2016 Task 1: Importance-Aware Compositional Approach to Short Text Similarity","year":2016,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Boeing","keywords":"SemEval; Computer science; Similarity (geometry); Word (group theory); Semantic similarity; Pairwise comparison; Natural language processing; Context (archaeology); Task (project management); Artificial intelligence; Rank (graph theory); Information retrieval; Image (mathematics); 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":[0.006982636,0.002971835,0.002926751,0.003526649,0.001930675,0.003639402,0.003420174,0.00319589,0.01760171],"category_scores_gemma":[0.02486821,0.0006774837,0.001804903,0.001948379,0.0007323142,0.005244943,0.00588816,0.002669772,0.01964787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001615547,"about_ca_system_score_gemma":0.002335812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005494095,"about_ca_topic_score_gemma":0.007446086,"domain_scores_codex":[0.9912224,0.003318415,0.0007562783,0.002344313,0.001878709,0.0004799603],"domain_scores_gemma":[0.9920772,0.00193869,0.0002842794,0.001913719,0.002787985,0.0009981407],"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":[0.003233619,0.001512287,0.004812924,0.002566162,0.0006009658,0.0009204295,0.001764625,0.009593137,0.04214521,0.004696369,0.3675034,0.5606508],"study_design_scores_gemma":[0.001524738,0.003093229,0.0193191,0.0002782241,0.0003608931,0.002586094,0.002637656,0.4251301,0.09324358,0.0253077,0.4259563,0.0005624657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2376282,0.006299894,0.3622037,0.003326999,0.008149443,0.006474854,0.06755663,0.269838,0.03852219],"genre_scores_gemma":[0.3641075,0.0007010647,0.4072018,0.0008963505,0.0009982447,0.003073043,0.1817096,0.01021205,0.03110041],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01760171,"threshold_uncertainty_score":0.05888355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03122763507974838,"score_gpt":0.2505197466858442,"score_spread":0.2192921116060958,"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."}}