{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007132316,0.001380029,0.001510991,0.003945894,0.001091018,0.001332463,0.001552497,0.000945747,0.004163922],"category_scores_gemma":[0.002771203,0.0004922586,0.001206889,0.003579216,0.0007989484,0.002685294,0.001522563,0.001152386,0.003483267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008739096,"about_ca_system_score_gemma":0.002164553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004960249,"about_ca_topic_score_gemma":0.006010144,"domain_scores_codex":[0.9977323,0.0004676711,0.0002076501,0.0008419509,0.00055799,0.0001924023],"domain_scores_gemma":[0.9983969,0.0004393947,0.0001670957,0.0002620432,0.0006667696,0.00006780624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005913322,0.0004154673,0.003172803,0.0006569815,0.000197056,0.0006436759,0.001355076,0.01859814,0.229191,0.01395904,0.016177,0.7150425],"study_design_scores_gemma":[0.0001829075,0.0006544263,0.007209769,0.00006271021,0.0003140397,0.001740256,0.001459889,0.7391043,0.1597439,0.02850566,0.06077005,0.0002522081],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02704712,0.0008217327,0.9607548,0.0001889529,0.00007645848,0.0002941395,0.0005806524,0.006504241,0.00373199],"genre_scores_gemma":[0.2928455,0.0006197646,0.6900895,0.0004564876,0.0002362069,0.000625644,0.006327012,0.001341659,0.007458213],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004960249,"threshold_uncertainty_score":0.01392972,"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."}}