{"id":"W2040916592","doi":"10.1145/2661829.2661887","title":"Robust Entity Linking via Random Walks","year":2014,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Computer science; Benchmark (surveying); Random walk; Information retrieval; Representation (politics); Context (archaeology); Knowledge base; Entity linking; Artificial intelligence; Popularity; Semantic similarity; Feature (linguistics); Base (topology); Similarity (geometry); Natural language processing; Task (project management)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00221307,0.001897284,0.002248983,0.006057114,0.001395312,0.002451526,0.003473537,0.00354263,0.003472918],"category_scores_gemma":[0.01277377,0.001268249,0.001701267,0.00699488,0.001069662,0.005647786,0.003199953,0.001910264,0.003695143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008390577,"about_ca_system_score_gemma":0.001244227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00489413,"about_ca_topic_score_gemma":0.007699351,"domain_scores_codex":[0.9970762,0.0008375329,0.0001381408,0.001140713,0.0006356271,0.0001718256],"domain_scores_gemma":[0.9932889,0.004096371,0.0006866868,0.001248238,0.0005166531,0.000163148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003254298,0.0002823186,0.003762423,0.0003196853,0.0003050701,0.0003998411,0.0002136903,0.5965362,0.005948596,0.02435079,0.01492215,0.3526339],"study_design_scores_gemma":[0.00001904525,0.00002796644,0.0002545163,0.00001435509,0.00002837062,0.0001211728,0.0000250111,0.9734355,0.002072302,0.02154708,0.002437837,0.00001671085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02455529,0.0007857798,0.9654885,0.0002734424,0.00006385981,0.0001770373,0.0006808777,0.005985387,0.001989807],"genre_scores_gemma":[0.3631898,0.0008295875,0.6142058,0.0004552801,0.0002449493,0.0003636819,0.007956197,0.001346069,0.01140862],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006057114,"threshold_uncertainty_score":0.01170403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02463689415480816,"score_gpt":0.2140333906751774,"score_spread":0.1893964965203692,"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."}}