{"id":"W40123913","doi":"","title":"AUTOMATICALLY CONSTRUCTING A LEXICON OF VERB PHRASE IDIOMATIC COMBINATIONS","year":2006,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Natural language processing; Computer science; Lexicon; Artificial intelligence; Linguistics; Flexibility (engineering); Phrase; Verb; Representation (politics); Class (philosophy); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001048708,0.001445672,0.001582759,0.006326457,0.001251207,0.003360229,0.001450121,0.0009577018,0.005356044],"category_scores_gemma":[0.008826998,0.001528859,0.0009530426,0.004314817,0.0008616865,0.004914063,0.002217692,0.001544738,0.003973915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00103977,"about_ca_system_score_gemma":0.001574484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001863464,"about_ca_topic_score_gemma":0.004204212,"domain_scores_codex":[0.9983408,0.0004593887,0.0002489133,0.0005208026,0.0003350993,0.00009488125],"domain_scores_gemma":[0.9962701,0.002126596,0.0003686601,0.0003711486,0.0007501605,0.000113414],"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":[0.000443098,0.0003789953,0.01784772,0.002509283,0.000339725,0.002625315,0.003045393,0.006791958,0.1270493,0.07625972,0.04869823,0.7140113],"study_design_scores_gemma":[0.0002827586,0.0004095125,0.02770223,0.0008595316,0.0007617206,0.008857287,0.004391519,0.4964699,0.1027831,0.143699,0.213292,0.0004913111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1403145,0.001084185,0.8012373,0.0008035279,0.0003255919,0.0007888006,0.01251425,0.01991653,0.02301531],"genre_scores_gemma":[0.3037088,0.0005811913,0.6690624,0.0002231746,0.0001059946,0.0005695942,0.02147145,0.002055417,0.00222196],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006326457,"threshold_uncertainty_score":0.01791775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006476881362674912,"score_gpt":0.2463076397073131,"score_spread":0.2398307583446382,"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."}}