{"id":"W1988366874","doi":"10.1111/j.1467-8640.2011.00401.x","title":"EFFECTIVE BIO-EVENT EXTRACTION USING TRIGGER WORDS AND SYNTACTIC DEPENDENCIES","year":2011,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Negation; Natural language processing; Event (particle physics); Heuristics; Artificial intelligence; Syntax; Parsing; Task (project management); Biomedical text mining; Annotation; Scope (computer science); Machine learning; Programming language; Text mining","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":[0.002724089,0.001545282,0.001269834,0.005684592,0.00118796,0.002432177,0.001454719,0.001491036,0.005068121],"category_scores_gemma":[0.01128762,0.0006723528,0.001526372,0.003007997,0.0007618777,0.005246876,0.002168485,0.001625115,0.003235477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00108899,"about_ca_system_score_gemma":0.002470921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001307531,"about_ca_topic_score_gemma":0.002366519,"domain_scores_codex":[0.9975101,0.0004505029,0.0004946021,0.0006905149,0.0007312,0.0001231514],"domain_scores_gemma":[0.9899819,0.006595883,0.001165072,0.0007027249,0.001380034,0.0001742975],"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":[0.0008958472,0.0004381663,0.01706776,0.002782919,0.0002348051,0.00301489,0.002280795,0.00895757,0.1274374,0.03917006,0.03785856,0.7598612],"study_design_scores_gemma":[0.0002453891,0.0002892079,0.01947497,0.0007085686,0.000667293,0.004877554,0.002119848,0.4116721,0.2929554,0.1391844,0.1275028,0.0003025158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07804275,0.001310945,0.8782843,0.001668914,0.0002396372,0.0008506085,0.009735822,0.02098969,0.008877316],"genre_scores_gemma":[0.2346368,0.0009853978,0.7396924,0.0004459299,0.0001652345,0.0004414119,0.01928806,0.001192119,0.003152723],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005684592,"threshold_uncertainty_score":0.01695454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05472830275994485,"score_gpt":0.336575975424708,"score_spread":0.2818476726647631,"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."}}