{"id":"W2469060249","doi":"10.18653/v1/n16-1108","title":"Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement","year":2016,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":249,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Science Foundation","keywords":"Computer science; Artificial intelligence; SemEval; Pairwise comparison; Similarity (geometry); Natural language processing; Semantic similarity; Word (group theory); Sentence; Semantics (computer science); Focus (optics); Artificial neural network; Selection (genetic algorithm); Mathematics","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":[0.001084474,0.001311028,0.0009068003,0.001176386,0.0004417759,0.0009593716,0.001936393,0.001179044,0.002591803],"category_scores_gemma":[0.003792297,0.000336842,0.0007605235,0.001634022,0.000473515,0.003506211,0.001568923,0.002259257,0.0009082591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001066135,"about_ca_system_score_gemma":0.0007343958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004366143,"about_ca_topic_score_gemma":0.006876721,"domain_scores_codex":[0.9991273,0.0002564343,0.00005369119,0.0002769439,0.0002025909,0.00008301179],"domain_scores_gemma":[0.9991449,0.0003997593,0.0001257799,0.0001212125,0.0001629202,0.00004543471],"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":[0.0005363186,0.0004491663,0.004463384,0.0003572618,0.0004131364,0.0002182715,0.0003972073,0.4341693,0.03605181,0.0351723,0.009083742,0.4786881],"study_design_scores_gemma":[0.000004175802,0.00001846977,0.0002387105,0.000003466236,0.00001217347,0.00001194931,0.00001180684,0.9865912,0.001556227,0.01120029,0.0003459932,0.000005574125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05118299,0.0006651219,0.9432856,0.0003003496,0.00007980983,0.00006523413,0.0003916463,0.001896408,0.002132826],"genre_scores_gemma":[0.8077451,0.0003656186,0.1859069,0.0002513886,0.0001241777,0.0002502176,0.00148864,0.0002180204,0.003649857],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004366143,"threshold_uncertainty_score":0.008681417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06594859114948542,"score_gpt":0.254434726655701,"score_spread":0.1884861355062156,"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."}}