{"id":"W1806995473","doi":"10.1007/11799573_42","title":"Learning Semantic Parsers: A Constraint Handling Rule Approach","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Parsing; Natural language processing; Artificial intelligence; Semantic compression; Semantic role labeling; Natural language; Semantic computing; Top-down parsing; Bottom-up parsing; Natural language understanding; Semantic analysis (machine learning); Frame (networking); Process (computing); S-attributed grammar; Word (group theory); Programming language; Semantic Web; Semantic technology; Linguistics","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":[0.004172383,0.001943069,0.002499961,0.003204894,0.001421131,0.004144214,0.008884546,0.003094843,0.01429953],"category_scores_gemma":[0.01795978,0.002285741,0.003932702,0.005187478,0.001875727,0.009979146,0.00376337,0.005698678,0.005707631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001267085,"about_ca_system_score_gemma":0.002576123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004541285,"about_ca_topic_score_gemma":0.008032367,"domain_scores_codex":[0.9961926,0.0009295369,0.0003866914,0.001264908,0.001006086,0.0002202243],"domain_scores_gemma":[0.9868466,0.009431124,0.0004133193,0.001632844,0.001469102,0.000207042],"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":[0.000204426,0.0004601518,0.001779739,0.0004263059,0.0002604688,0.0005259989,0.0002689483,0.06462914,0.004268699,0.04953313,0.02666637,0.8509766],"study_design_scores_gemma":[0.0000957935,0.00008638422,0.0005191794,0.0001092993,0.0002605614,0.0002873789,0.000191208,0.7885466,0.009739836,0.1865419,0.01354421,0.0000777345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005443966,0.0002206636,0.985535,0.0003586929,0.000051682,0.0001469384,0.0007500236,0.004934599,0.002558391],"genre_scores_gemma":[0.0515501,0.0003215805,0.9393959,0.0002693472,0.00008229068,0.0002210777,0.004520514,0.0008321791,0.002807117],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01429953,"threshold_uncertainty_score":0.04783666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01399734847342522,"score_gpt":0.2423561569739967,"score_spread":0.2283588085005714,"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."}}