{"id":"W1544240449","doi":"10.1007/3-540-45486-1_4","title":"Using Noun Phrase Heads to Extract Document Keyphrases","year":2000,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":236,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Noun phrase; Automatic summarization; Natural language processing; Artificial intelligence; Phrase; Task (project management); Extractor; Head (geology); Noun; Proper noun; Information retrieval; Linguistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007002905,0.00195195,0.001196959,0.006049979,0.001044377,0.002798881,0.0007317809,0.001057562,0.01189557],"category_scores_gemma":[0.003669307,0.0008113304,0.001060241,0.004638606,0.000534061,0.003023813,0.001279289,0.001326145,0.0214102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006419757,"about_ca_system_score_gemma":0.001543352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002821813,"about_ca_topic_score_gemma":0.003976644,"domain_scores_codex":[0.9993348,0.00005795266,0.00008623162,0.0002013485,0.0002372135,0.00008243225],"domain_scores_gemma":[0.9964753,0.001404814,0.0003102396,0.0003016573,0.001356605,0.0001513584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006315422,0.0001122363,0.002544596,0.001735001,0.00009180542,0.001176117,0.0008391306,0.0008613307,0.2014068,0.004592314,0.02451679,0.7614924],"study_design_scores_gemma":[0.000301813,0.0009526252,0.02604275,0.000647313,0.0008528254,0.005216947,0.003462117,0.1082341,0.5635957,0.02995366,0.2602973,0.0004428894],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06625529,0.003490966,0.8425766,0.0005862986,0.0008535942,0.0009565554,0.01765587,0.05235197,0.01527291],"genre_scores_gemma":[0.1452871,0.002840224,0.8003367,0.0002240728,0.0004122438,0.0004819517,0.02872278,0.00375346,0.01794153],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01189557,"threshold_uncertainty_score":0.03979462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02587671481000824,"score_gpt":0.3118338681191701,"score_spread":0.2859571533091619,"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."}}