{"id":"W1519642919","doi":"10.1007/978-3-540-30211-7_58","title":"A Nearest-Neighbor Method for Resolving PP-Attachment Ambiguity","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Pointwise; Pointwise mutual information; Cosine similarity; k-nearest neighbors algorithm; Ambiguity; Similarity (geometry); Computer science; Phrase; Artificial intelligence; Task (project management); Trigonometric functions; Word (group theory); Similarity measure; Pattern recognition (psychology); Natural language processing; Algorithm; Mathematics; Mutual information; Image (mathematics); Engineering","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":[0.001540672,0.0009579175,0.001333489,0.002634365,0.001876641,0.001939839,0.003114798,0.002022863,0.01392623],"category_scores_gemma":[0.006155824,0.000703274,0.001199407,0.003351637,0.0005919092,0.003214667,0.002100193,0.001562613,0.007362845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005542982,"about_ca_system_score_gemma":0.001389493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006666856,"about_ca_topic_score_gemma":0.01313051,"domain_scores_codex":[0.9981206,0.00031774,0.0001264009,0.0004462794,0.0008447027,0.0001443712],"domain_scores_gemma":[0.9977914,0.0005636287,0.00009400774,0.0006116948,0.0008633413,0.0000759911],"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":[0.0002206887,0.0001578959,0.0008544073,0.0001483678,0.00005795348,0.0001882466,0.0002114883,0.009751451,0.009175845,0.01781154,0.01857591,0.9428462],"study_design_scores_gemma":[0.00008389096,0.00009249919,0.001476235,0.00006078336,0.0001472781,0.0008752204,0.0004134848,0.8613409,0.01964124,0.07146075,0.04429098,0.0001166771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008211653,0.0004661038,0.982172,0.0001395045,0.0002803372,0.0001319514,0.0004244605,0.003036687,0.005137422],"genre_scores_gemma":[0.06684767,0.0002915499,0.9186531,0.0001487541,0.0001441129,0.0001138748,0.001549051,0.0006720246,0.01157989],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01392623,"threshold_uncertainty_score":0.04658788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02103100848215812,"score_gpt":0.3217057140259904,"score_spread":0.3006747055438323,"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."}}