{"id":"W2949446888","doi":"10.48550/arxiv.1807.10805","title":"Improving Neural Sequence Labelling using Additional Linguistic Information","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Chunking (psychology); Computer science; Natural language processing; Sequence labeling; Artificial intelligence; Sequence (biology); Named-entity recognition; Word (group theory); Labelling; Sentence; Benchmark (surveying); Task (project management); Linguistics","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":[0.001505574,0.001263095,0.001168867,0.00131361,0.0006787475,0.001092212,0.002175061,0.001990465,0.003359422],"category_scores_gemma":[0.007125259,0.0005764672,0.001078849,0.001375203,0.0006793205,0.004755227,0.001493147,0.002605011,0.003267528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001234825,"about_ca_system_score_gemma":0.001663971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007020582,"about_ca_topic_score_gemma":0.01426017,"domain_scores_codex":[0.9992638,0.0002062344,0.00004816384,0.0002951707,0.0001206438,0.00006593527],"domain_scores_gemma":[0.9965034,0.001843878,0.000212295,0.0006413469,0.0006776816,0.0001213852],"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":[0.000357676,0.0004240748,0.00281117,0.0003666635,0.000113304,0.0002099525,0.0004197822,0.2883525,0.01814332,0.01401798,0.01790299,0.6568805],"study_design_scores_gemma":[0.00001175155,0.00004021535,0.0002482003,0.0000221999,0.00002192605,0.00004403707,0.00003001547,0.9808882,0.00321298,0.01318969,0.002277097,0.00001374119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04142185,0.001078473,0.9463962,0.0006129439,0.0002486492,0.0001055741,0.0005623175,0.00574572,0.003828206],"genre_scores_gemma":[0.5161473,0.0009659034,0.457184,0.001015774,0.0003017704,0.0004241516,0.006579933,0.0008103182,0.01657085],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007020582,"threshold_uncertainty_score":0.01395947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1136631976768186,"score_gpt":0.2031024741407496,"score_spread":0.08943927646393099,"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."}}