{"id":"W2964823609","doi":"10.1609/aaai.v33i01.330110075","title":"Sequence to Sequence Learning for Query Expansion","year":2019,"lang":"en","type":"article","venue":"","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Sequence (biology); Query expansion; Sentence; Information retrieval; Set (abstract data type); Space (punctuation); Artificial intelligence; Natural language processing; Artificial neural network","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002390718,0.0000920073,0.0001267201,0.00006100503,0.00007300622,0.00009834798,0.0006327898,0.00004363309,0.00002527773],"category_scores_gemma":[0.0001353962,0.00007361473,0.00004391202,0.0001831121,0.00001438634,0.0003851684,0.0001888726,0.0000696872,0.0004329834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002995014,"about_ca_system_score_gemma":0.00006435189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009054629,"about_ca_topic_score_gemma":0.00001552027,"domain_scores_codex":[0.9990565,0.00002514398,0.0001265403,0.0003687542,0.0001501498,0.0002728897],"domain_scores_gemma":[0.9992508,0.0001933251,0.00003322008,0.000386892,0.00007139392,0.00006435064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003176969,0.00005208327,0.02238812,0.000108341,0.00001842134,0.00002634477,0.002716609,0.006302183,0.3815487,0.3617077,0.004186872,0.2209129],"study_design_scores_gemma":[0.001452617,0.001870783,0.01728525,0.0002756903,0.00001215544,0.0001177946,0.001629405,0.6676255,0.1921983,0.02560013,0.09027249,0.001659875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2998892,0.00002318755,0.6937033,0.001709675,0.0004094325,0.0002792463,2.561113e-7,0.0003229817,0.003662723],"genre_scores_gemma":[0.8588364,0.000004326382,0.1357049,0.00116101,0.00002523298,0.00002098245,7.914192e-7,0.000004777828,0.004241562],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6613234,"threshold_uncertainty_score":0.556527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05448195730448642,"score_gpt":0.3051787507567879,"score_spread":0.2506967934523014,"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."}}