{"id":"W2115056464","doi":"","title":"Vector Space Model for Adaptation in Statistical Machine Translation","year":2013,"lang":"en","type":"article","venue":"NPARC","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Phrase; Artificial intelligence; Machine translation; Vector space model; Feature vector; Support vector machine; Translation (biology); Natural language processing; Set (abstract data type); NIST","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001069889,0.00006008485,0.0000691128,0.00006019264,0.00002548369,0.00006526374,0.0001986926,0.00003954619,0.00001757417],"category_scores_gemma":[0.0000497361,0.00005337737,0.00001376353,0.0001092178,0.00001146539,0.0004145537,0.00001766991,0.00007308511,0.000006286214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002778797,"about_ca_system_score_gemma":0.00002956357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003897698,"about_ca_topic_score_gemma":0.00004192957,"domain_scores_codex":[0.9994735,0.00001746963,0.0001079228,0.0001638162,0.0001095269,0.0001278174],"domain_scores_gemma":[0.9996858,0.00008772064,0.00002810636,0.0001211046,0.00004791875,0.00002936938],"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":[0.00001272045,0.00004104876,0.00002034537,0.00004021327,0.000002282195,0.000001494651,0.00251387,0.0007445749,0.03980055,0.4047315,0.001052494,0.5510389],"study_design_scores_gemma":[0.0001045308,0.00001786946,0.00003999895,0.000006469738,7.786082e-7,5.752717e-7,0.00000308279,0.6908273,0.001404128,0.3075295,0.00001775935,0.00004803852],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00064663,0.0001346907,0.996326,0.002153461,0.00002582862,0.0002869356,0.000005798783,0.0001611171,0.0002595506],"genre_scores_gemma":[0.4744193,9.544341e-7,0.5254292,0.0000536708,0.000006381162,0.00004530141,0.000005168755,0.000003162025,0.00003676151],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6900827,"threshold_uncertainty_score":0.2176666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02700135464842249,"score_gpt":0.2812503155076617,"score_spread":0.2542489608592392,"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."}}