{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001616557,0.0009185975,0.0006581396,0.001265177,0.0003234911,0.0005626034,0.001130364,0.0009783426,0.006755598],"category_scores_gemma":[0.006368454,0.0002708438,0.0006412987,0.001164173,0.0005655966,0.003305695,0.000933497,0.001298142,0.002041676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008827384,"about_ca_system_score_gemma":0.0009421585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004058009,"about_ca_topic_score_gemma":0.005445317,"domain_scores_codex":[0.9989095,0.0003609206,0.00008616656,0.000331946,0.0002227129,0.00008878401],"domain_scores_gemma":[0.9979339,0.00117017,0.0001151787,0.0002813933,0.000434258,0.00006525326],"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":[0.0004923831,0.0005328734,0.002114942,0.0005566318,0.0001051692,0.0001749546,0.0003132448,0.1121661,0.03814206,0.0176357,0.01183489,0.8159311],"study_design_scores_gemma":[0.00003155732,0.0002066139,0.0004645112,0.00002157377,0.00002581631,0.0001185969,0.00006716605,0.9662933,0.01068021,0.01704443,0.005031016,0.00001511945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05969888,0.001444507,0.9263701,0.0005596201,0.000147172,0.0003520853,0.0005798889,0.005316087,0.005531784],"genre_scores_gemma":[0.560606,0.0007729263,0.4273961,0.0006005548,0.0001917446,0.000420434,0.002350906,0.0003961963,0.007265096],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006755598,"threshold_uncertainty_score":0.0225997,"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."}}