{"id":"W2596484740","doi":"10.18653/v1/e17-1006","title":"Learning Compositionality Functions on Word Embeddings for Modelling Attribute Meaning in Adjective-Noun Phrases","year":2017,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universität Bielefeld; Bundesministerium für Bildung und Forschung; Atomic Energy of Canada Limited; Deutsche Forschungsgemeinschaft","keywords":"Principle of compositionality; Adjective; Computer science; Natural language processing; Artificial intelligence; Noun; Noun phrase; Specifier; Meaning (existential); Word (group theory); Linguistics; Psychology; Philosophy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005141736,0.0001459267,0.000175762,0.0001377774,0.000971571,0.0005606916,0.0007760805,0.00007256868,0.000007114199],"category_scores_gemma":[0.0002328418,0.0001344589,0.00007315386,0.0001226853,0.00004555031,0.00101696,0.0002312196,0.00031227,0.000007694781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001254726,"about_ca_system_score_gemma":0.00004042534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001952427,"about_ca_topic_score_gemma":0.00004602916,"domain_scores_codex":[0.9988254,0.00004236381,0.0002004281,0.000443446,0.0002114892,0.000276924],"domain_scores_gemma":[0.998932,0.0002771497,0.0001737042,0.0004184449,0.0001495867,0.00004911096],"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":[0.0002642773,0.0004031124,0.007067554,0.000132958,0.00007252342,0.00003814881,0.003273486,0.06385572,0.005549441,0.8356132,0.001029178,0.08270039],"study_design_scores_gemma":[0.0005292165,0.0002246159,0.0004732641,0.0002985285,0.000008537648,0.00001006524,0.0001085183,0.8945979,0.01197397,0.09066144,0.000720938,0.00039301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02181743,0.00008373457,0.9745995,0.0007729148,0.0001176805,0.0002112629,0.000004709793,0.0005398355,0.001852923],"genre_scores_gemma":[0.6638869,0.000002587025,0.3355724,0.0001128528,0.0000457103,0.00003630306,0.000007921338,0.000007775101,0.0003275576],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8307422,"threshold_uncertainty_score":0.7472637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04482606863531863,"score_gpt":0.3216821619894747,"score_spread":0.2768560933541561,"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."}}