{"id":"W2602143361","doi":"10.1109/icsc.2017.9","title":"A Context-Aware Approach for the Identification of Complex Words in Natural Language Texts","year":2017,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Identification (biology); Natural language processing; Artificial intelligence; Context (archaeology); SemEval; Word (group theory); Natural language; Natural language understanding; Natural (archaeology); Word identification; Linguistics; Word recognition; Task (project management)","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":[0.001504023,0.001420332,0.0008894666,0.005642155,0.0008566272,0.001262859,0.000793244,0.0009874532,0.001358947],"category_scores_gemma":[0.007273526,0.0003777584,0.0009583644,0.002638306,0.0003924039,0.003252456,0.001396155,0.001365351,0.00164351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004118493,"about_ca_system_score_gemma":0.0007996346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002880789,"about_ca_topic_score_gemma":0.006388978,"domain_scores_codex":[0.9978008,0.0006743545,0.0001884719,0.0007952148,0.0004076311,0.0001334312],"domain_scores_gemma":[0.994346,0.003266958,0.0005470866,0.0005807398,0.001059259,0.0002000967],"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":[0.001048838,0.0005157696,0.02270185,0.001231593,0.0004847488,0.0004317436,0.001332787,0.009502917,0.0648175,0.00313607,0.009566467,0.8852298],"study_design_scores_gemma":[0.0002231513,0.001351146,0.06805176,0.0004216835,0.001169584,0.002939457,0.002093944,0.7847331,0.0684526,0.02359717,0.04659983,0.0003665743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3502745,0.01799682,0.6033819,0.0007343775,0.0007128302,0.0009297716,0.003735792,0.01330047,0.008933594],"genre_scores_gemma":[0.6862484,0.001759245,0.3029953,0.000253099,0.0005978743,0.0003840998,0.004673754,0.0003661309,0.002722134],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005642155,"threshold_uncertainty_score":0.007954121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05628516500447647,"score_gpt":0.3162714223898345,"score_spread":0.259986257385358,"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."}}