{"id":"W2492159292","doi":"10.4018/978-1-60960-625-1.ch002","title":"An Overview of Shallow and Deep Natural Language Processing for Ontology Learning","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University; Simon Fraser University","funders":"","keywords":"Computer science; Ontology; Natural language processing; Artificial intelligence; Dependency (UML); Deep learning; Task (project management); Semantic Web; Process (computing); Programming language; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001996347,0.0003770156,0.0005532328,0.00009924916,0.00010949,0.00012032,0.0009911625,0.000410472,0.000004375736],"category_scores_gemma":[0.00004214916,0.000339598,0.0001234771,0.00002642686,0.0001337445,0.0002492218,0.0003031519,0.0004077351,0.000001642682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007594415,"about_ca_system_score_gemma":0.000134487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000827997,"about_ca_topic_score_gemma":0.0002241058,"domain_scores_codex":[0.9984193,0.00003006946,0.0003560937,0.0006384631,0.0002283426,0.0003277246],"domain_scores_gemma":[0.9987472,0.00003897893,0.0004504972,0.0004470416,0.0002142025,0.0001020792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001599643,0.000005030383,0.000009236534,0.0002900324,0.00001773285,0.0000232459,0.0005233151,9.368556e-8,0.0001568579,0.6637554,0.000008810317,0.3351943],"study_design_scores_gemma":[0.0004398664,0.000546324,0.00003001262,0.001164769,0.000102453,0.0002661842,0.00002963241,0.003807044,0.001117752,0.9891798,0.002426028,0.0008900802],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0003210482,0.3997082,0.1920512,0.0000717072,0.0004643882,0.001242244,0.00004076716,0.001760168,0.4043402],"genre_scores_gemma":[0.5773653,0.00005263446,0.4142232,0.0004180415,0.0001540137,0.00003155126,0.00001055889,0.00006427211,0.007680459],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5770442,"threshold_uncertainty_score":0.9999056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03124175699176025,"score_gpt":0.3099063973697564,"score_spread":0.2786646403779962,"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."}}