Statements (59)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:test_subjects
|
| gptkbp:analyzes |
gptkb:matplotlib
gptkb:seaborn py LDAvis |
| gptkbp:applies_to |
text data
|
| gptkbp:based_on |
Bayesian inference
|
| gptkbp:can_be_parallelized_using |
Gibbs sampling
variational inference |
| gptkbp:can_be_used_in |
gptkb:machine_learning
natural language processing information retrieval text mining |
| gptkbp:developed_by |
gptkb:David_Blei
gptkb:Michael_I._Jordan gptkb:Andrew_Ng |
| gptkbp:has_implications_for |
documents are mixtures of topics
topics are distributions over words |
| https://www.w3.org/2000/01/rdf-schema#label |
Latent Dirichlet Allocation
|
| gptkbp:input_output |
document-topic distributions
topic distributions |
| gptkbp:is_a |
generative probabilistic model
|
| gptkbp:is_applicable_to |
large datasets
streaming data |
| gptkbp:is_evaluated_by |
perplexity
coherence score |
| gptkbp:is_implemented_in |
gptkb:Java
gptkb:Python gptkb:R |
| gptkbp:is_part_of |
gptkb:Statistician
statistical modeling unsupervised learning |
| gptkbp:is_related_to |
Hierarchical Dirichlet Process
Non-negative Matrix Factorization |
| gptkbp:is_used_in |
gptkb:academic_research
customer feedback analysis social media analysis news categorization |
| gptkbp:is_used_to |
improve search results
extract features from text reduce dimensionality of text data summarize large text corpora |
| gptkbp:requires |
number of topics
|
| gptkbp:sensitivity |
initialization
|
| gptkbp:technique |
information extraction
data mining text analysis semantic analysis pattern recognition feature extraction content recommendation knowledge discovery text classification document clustering topic discovery |
| gptkbp:tuning |
hyperparameters
|
| gptkbp:used_for |
discovering abstract topics
|
| gptkbp:bfsParent |
gptkb:Natural_Language_Processing
gptkb:David_Blei |
| gptkbp:bfsLayer |
4
|