{"id":"W2061023833","doi":"10.3115/1075218.1075232","title":"An unsupervised approach to prepositional phrase attachment using contextually similar words","year":2000,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Natural language processing; Ambiguity; Artificial intelligence; Phrase; Parsing; Noun phrase; Natural language; Heuristic; Unsupervised learning; Process (computing); Dependency grammar; Noun","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002377612,0.0001738772,0.000149368,0.0001009688,0.0001585113,0.0003345279,0.001008292,0.0000704311,0.0002124588],"category_scores_gemma":[0.000008723676,0.0001479909,0.00004760388,0.0003858264,0.00002800302,0.0008320232,0.0001147395,0.0001205423,0.00001903338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009053537,"about_ca_system_score_gemma":0.00007955761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005785124,"about_ca_topic_score_gemma":0.000002354549,"domain_scores_codex":[0.9985,0.00006764786,0.0002220917,0.0005326027,0.0003878605,0.0002898076],"domain_scores_gemma":[0.9990721,0.00001695934,0.00003091752,0.0005951938,0.00008605201,0.0001988096],"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":[0.000213701,0.002930162,0.0007017835,0.0001006359,0.0001066505,0.0001678544,0.01094417,0.008774305,0.1513662,0.1436672,0.005476222,0.6755512],"study_design_scores_gemma":[0.0008141226,0.0004162149,0.0002994196,0.0001065628,0.00002228364,0.0002335594,0.0001249851,0.9272352,0.05627824,0.01138124,0.001945166,0.001143021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05121843,0.0002151764,0.9396941,0.0003702221,0.00003134866,0.0003018556,0.000003347628,0.0009095885,0.007255894],"genre_scores_gemma":[0.4461502,7.632478e-7,0.5516737,0.001847175,0.00003707969,0.00001485731,0.000006587768,0.000007135369,0.0002625048],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9184609,"threshold_uncertainty_score":0.6034892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02244744522578051,"score_gpt":0.3040752623063306,"score_spread":0.2816278170805501,"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."}}