{"id":"W2001214180","doi":"10.5539/cis.v3n1p168","title":"Automatic Recognition of Focus and Interrogative Word in Chinese Question for Classification","year":2010,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Interrogative; Computer science; Focus (optics); Artificial intelligence; Natural language processing; Interrogative word; CRFS; Conditional random field; Word (group theory); Parsing; Part of speech; Text segmentation; Segmentation; Linguistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001791826,0.0006753416,0.0007458304,0.00257933,0.0006597551,0.0007451657,0.0007337054,0.0006421764,0.002417521],"category_scores_gemma":[0.004786194,0.0002293055,0.0006248075,0.001117496,0.0004850354,0.002017952,0.0009708784,0.0005368674,0.001205895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006901678,"about_ca_system_score_gemma":0.00104543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009937418,"about_ca_topic_score_gemma":0.005305432,"domain_scores_codex":[0.9987852,0.0003080658,0.0001194563,0.0004103726,0.000190302,0.000186706],"domain_scores_gemma":[0.995261,0.001772851,0.0003791648,0.0005116325,0.001869104,0.000206203],"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":[0.0008725573,0.00029747,0.08024506,0.0009481461,0.00008844662,0.0007194299,0.006121106,0.003948937,0.2471591,0.007403787,0.02070972,0.6314863],"study_design_scores_gemma":[0.0001396802,0.0006718285,0.2689075,0.0001150957,0.0003472434,0.00163484,0.003387985,0.4300582,0.2537777,0.008299848,0.03241482,0.0002453207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7564893,0.00105226,0.2232957,0.000546534,0.0001725954,0.0006250507,0.003682354,0.00633624,0.007799871],"genre_scores_gemma":[0.9269786,0.0002012842,0.06309111,0.0001133256,0.00006136555,0.0002512379,0.006205612,0.0001496138,0.002947788],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009937418,"threshold_uncertainty_score":0.01975912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02397677736884165,"score_gpt":0.2882214725990372,"score_spread":0.2642446952301956,"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."}}