{"id":"W2548230849","doi":"10.1075/nlp.2.15mey","title":"Extracting knowledge-rich contexts for terminography","year":2001,"lang":"en","type":"book-chapter","venue":"Natural language processing","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":215,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Construct (python library); Computer science; Paralanguage; Domain knowledge; Domain (mathematical analysis); Knowledge extraction; Field (mathematics); Context (archaeology); Knowledge management; Natural language processing; Data science; Artificial intelligence; Psychology; Communication; Geography; Mathematics","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":[0.001388135,0.0006908762,0.0005911522,0.00303333,0.001131591,0.002879164,0.00116295,0.0007922247,0.004759598],"category_scores_gemma":[0.005998624,0.0007256229,0.000913582,0.003312267,0.001411841,0.0072093,0.002355797,0.001868099,0.003611285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000786385,"about_ca_system_score_gemma":0.001068362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005800364,"about_ca_topic_score_gemma":0.001760938,"domain_scores_codex":[0.9990559,0.0003599697,0.00009406748,0.000157749,0.0002960226,0.00003618616],"domain_scores_gemma":[0.9973213,0.001852538,0.00009813302,0.00042182,0.0002590098,0.00004717746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006190898,0.00003753115,0.001043282,0.001316286,0.00004172568,0.0006070177,0.003521673,0.003542223,0.0102667,0.4065999,0.02007541,0.5528864],"study_design_scores_gemma":[0.00002108273,0.00003236941,0.001284014,0.001198116,0.00007026547,0.001368206,0.00097531,0.02688494,0.01558705,0.5650083,0.3875002,0.00007004268],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006694648,0.005613583,0.9614586,0.000645461,0.0002187884,0.0001626671,0.0004666967,0.001202827,0.02353679],"genre_scores_gemma":[0.03957,0.005195871,0.9443949,0.0001824101,0.0001314663,0.0002233798,0.001525029,0.0004938144,0.008283064],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004759598,"threshold_uncertainty_score":0.01592249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01563003246822913,"score_gpt":0.3028685290154244,"score_spread":0.2872384965471953,"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."}}