{"id":"W2142989929","doi":"10.1007/978-3-642-21043-3_10","title":"Automatic Semantic Web Annotation of Named Entities","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Information retrieval; Annotation; Entity linking; Semantic similarity; Semantic Web Stack; Semantic Web; RDF; Social Semantic Web; Set (abstract data type); Context (archaeology); Semantic annotation; Natural language processing; Artificial intelligence; Knowledge base; Programming language","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":[0.001459693,0.0007969892,0.0008613491,0.005505829,0.001460188,0.002875522,0.001521687,0.001149892,0.008370196],"category_scores_gemma":[0.003856858,0.0006657033,0.001186205,0.005452385,0.0006887659,0.005477999,0.002521056,0.001460535,0.006449016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007682848,"about_ca_system_score_gemma":0.001588493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002988437,"about_ca_topic_score_gemma":0.005347576,"domain_scores_codex":[0.998826,0.000240758,0.0001494087,0.0002292488,0.0004837411,0.00007082932],"domain_scores_gemma":[0.9975526,0.0009642223,0.0001547073,0.0005927454,0.0006669884,0.00006869848],"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":[0.0002755004,0.0002988629,0.002203249,0.00143866,0.0001135221,0.0009595525,0.000909754,0.006185897,0.03322515,0.104518,0.09161276,0.758259],"study_design_scores_gemma":[0.00006836054,0.00006689661,0.004658185,0.0009051444,0.0002612844,0.001511757,0.0008562155,0.2417978,0.08927034,0.2257879,0.4346799,0.0001362055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02201545,0.002129979,0.9202527,0.000825908,0.0006145754,0.0002466609,0.008783657,0.02261597,0.02251505],"genre_scores_gemma":[0.1159197,0.002662494,0.8111941,0.0003429864,0.0002040491,0.0002943002,0.04945965,0.003057943,0.01686471],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008370196,"threshold_uncertainty_score":0.02800107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0138895398202253,"score_gpt":0.2471947924643962,"score_spread":0.2333052526441709,"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."}}