{"id":"W2062246499","doi":"10.5715/jnlp.18.217","title":"Construction of Context Models for Word Sense Disambiguation","year":2011,"lang":"en","type":"article","venue":"Journal of Natural Language Processing","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Ministry of Education, Culture, Sports, Science and Technology","keywords":"Word-sense disambiguation; Word (group theory); Context (archaeology); Computer science; SemEval; Natural language processing; Linguistics; Artificial intelligence; History; Philosophy; Engineering","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":[0.002108113,0.001839285,0.001782503,0.004371137,0.001663098,0.001797036,0.001710995,0.001337949,0.001701434],"category_scores_gemma":[0.01252292,0.00147506,0.002271261,0.003018675,0.001098771,0.005472577,0.002680236,0.002521374,0.001029945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009183139,"about_ca_system_score_gemma":0.001767806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00410116,"about_ca_topic_score_gemma":0.008894399,"domain_scores_codex":[0.9977419,0.0008878513,0.000153474,0.0007341198,0.0003645629,0.0001181627],"domain_scores_gemma":[0.9962,0.002347955,0.0002860012,0.0005329805,0.0004714369,0.0001615673],"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":[0.0005295278,0.0002905964,0.006747219,0.0006237096,0.0006614671,0.0006268719,0.001250729,0.319558,0.01300515,0.1150449,0.008062129,0.5335999],"study_design_scores_gemma":[0.00003716352,0.00004951888,0.0006578094,0.00006449044,0.00009412441,0.0001525482,0.0001058237,0.8681103,0.00261681,0.1225593,0.005503129,0.0000490814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0101184,0.0008271072,0.9868897,0.0001515071,0.0000782435,0.00007927974,0.0002004969,0.0009084117,0.0007468697],"genre_scores_gemma":[0.2768868,0.001269284,0.717652,0.0002865409,0.0002184887,0.0005450605,0.00136901,0.0005145698,0.001258269],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004371137,"threshold_uncertainty_score":0.01114887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02621211769329632,"score_gpt":0.2813093315516963,"score_spread":0.2550972138584,"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."}}