{"id":"W3036116903","doi":"10.1162/tacl_a_00316","title":"Learning Lexical Subspaces in a Distributional Vector Space","year":2020,"lang":"en","type":"article","venue":"Transactions of the Association for Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Distributional semantics; Linear subspace; Artificial intelligence; Natural language processing; Similarity (geometry); Vector space; Word (group theory); Space (punctuation); Relation (database); Semantics (computer science); Semantic similarity; Suite; Code (set theory); Linguistics; Programming language; Data mining; Mathematics","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":[0.000233882,0.00008198959,0.000131857,0.00004650513,0.0001585188,0.00004953048,0.0003923585,0.00007105937,0.000002814784],"category_scores_gemma":[0.002845531,0.00007393916,0.0001075444,0.0004778953,0.00002514793,0.0000669315,0.00002281582,0.0002407528,0.000001748343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000174635,"about_ca_system_score_gemma":0.0001357632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000128929,"about_ca_topic_score_gemma":0.000005695196,"domain_scores_codex":[0.9990405,0.00006387007,0.0002471537,0.0001663206,0.0003482548,0.000133842],"domain_scores_gemma":[0.9984587,0.000642937,0.0002389261,0.0000752728,0.0005465141,0.00003768622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003882823,0.0001268299,0.006541947,0.00007877633,0.00005648837,7.748241e-7,0.001081908,0.2462142,0.0002692057,0.7443233,0.0005079906,0.0007598134],"study_design_scores_gemma":[0.0007952275,0.0001713885,0.005947527,0.00005705436,0.00003833609,0.000001558764,0.0000476996,0.8271922,0.004132099,0.1559251,0.005437665,0.0002541304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007746071,0.00006702999,0.9925947,0.005869948,0.0002300905,0.0001662771,0.00005970983,0.0001271125,0.0001105517],"genre_scores_gemma":[0.8169653,0.0000012942,0.1827545,0.00008987429,0.00008126389,0.00001002835,0.00001726722,0.000005269529,0.00007512144],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8161907,"threshold_uncertainty_score":0.3406571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01387567187832345,"score_gpt":0.2681135972948576,"score_spread":0.2542379254165342,"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."}}