{"id":"W2598150924","doi":"10.1142/s2425038416300056","title":"Methods and resources for computing semantic relatedness","year":2017,"lang":"en","type":"article","venue":"Encyclopedia with Semantic Computing and Robotic Intelligence","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"WordNet; Computer science; Information retrieval; Semantic similarity; Key (lock); Section (typography); Selection (genetic algorithm); Resource (disambiguation); Knowledge base; Similarity (geometry); Representation (politics); Natural language processing; Artificial intelligence","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","sts"],"consensus_categories":[],"category_scores_codex":[0.001431975,0.0003499433,0.00048074,0.0001532408,0.001733583,0.000937126,0.001277462,0.0001391885,0.000001065557],"category_scores_gemma":[0.0006327911,0.0002670721,0.00005142096,0.0001844739,0.0003850947,0.0004239822,0.001020954,0.0003867455,0.000001840345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002121467,"about_ca_system_score_gemma":0.00005297145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001480922,"about_ca_topic_score_gemma":0.00001431211,"domain_scores_codex":[0.9977384,0.0001246535,0.0004477201,0.0008961621,0.0002367539,0.00055635],"domain_scores_gemma":[0.9973169,0.0009979182,0.0004699562,0.0008660352,0.0001910389,0.0001581712],"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":[0.00002887884,0.00006003878,0.005132196,0.0007124582,0.00007938395,0.00008642778,0.01048512,0.001432537,0.0001933866,0.04131131,0.00006162408,0.9404166],"study_design_scores_gemma":[0.0002527929,0.0002793093,0.002975199,0.001434588,0.00007971101,0.0004309738,0.000300347,0.9622969,0.001508,0.0294455,0.0002798273,0.0007168506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02575208,0.002866664,0.9688967,0.0008081812,0.0004631956,0.0004051076,3.687799e-7,0.0004239368,0.0003836976],"genre_scores_gemma":[0.4692749,0.00008960722,0.5303307,0.00006050495,0.00009054573,0.000002961907,4.575696e-7,0.00001691483,0.0001334789],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9608644,"threshold_uncertainty_score":0.9999781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01801676500873844,"score_gpt":0.3327686051755747,"score_spread":0.3147518401668362,"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."}}