{"id":"W2120158994","doi":"10.5539/cis.v5n1p13","title":"Unsupervised Query Segmentation Using Monolingual Word Alignment Method","year":2011,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Query expansion; Segmentation; Artificial intelligence; Query language; Web query classification; Natural language processing; Query optimization; Sargable; Text segmentation; RDF query language; Word (group theory); Language model; Market segmentation; Query by Example; Web search query; Information retrieval; Search engine","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":[0.0007697401,0.0001036056,0.00009141884,0.0003315419,0.0002598491,0.0004697357,0.0006985321,0.00003320904,0.000004411146],"category_scores_gemma":[0.00001858687,0.00008781322,0.00001910462,0.0007093105,0.0001033331,0.01215929,0.0004345445,0.0000749403,0.000006764637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006200512,"about_ca_system_score_gemma":0.0001085523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003711889,"about_ca_topic_score_gemma":2.17175e-7,"domain_scores_codex":[0.9989398,0.00002865529,0.0002648318,0.0002026948,0.000355673,0.0002083503],"domain_scores_gemma":[0.9993153,0.00002148944,0.0001262361,0.0002523646,0.0001930329,0.00009159741],"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":[0.000004701611,0.00001656226,0.0002255893,0.00002492447,0.00000371458,0.000001748253,0.01885503,0.00006714069,0.00422387,0.05464512,0.00004264325,0.9218889],"study_design_scores_gemma":[0.0002220848,0.00006476897,0.001291174,0.00003989989,0.000003840876,0.00006548896,0.000140787,0.8942273,0.09550213,0.007939102,0.0002606254,0.0002427508],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02353321,0.00007153479,0.9753351,0.00003902297,0.0002397103,0.0001289163,5.705666e-7,0.0002232482,0.0004286364],"genre_scores_gemma":[0.1856298,0.000008699863,0.8135694,0.0007634838,0.00002044483,0.000003595423,0.000001005825,0.000001648915,0.000001915999],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9216462,"threshold_uncertainty_score":0.8815192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03692806809415005,"score_gpt":0.3103869872140255,"score_spread":0.2734589191198755,"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."}}