S-LDA: Documents Classification Enrichment for Information Retrieval.
Amani DrissiAnis TissaouiSalma SassiRichard ChbeirAbderrazak JemaiPublished in: ICCCI (CCIS Volume) (2022)
Keyphrases
- information retrieval
- document collections
- information retrieval systems
- document classification
- automatic categorization
- topic modeling
- feature extraction
- document retrieval
- vector space model
- relevant documents
- latent semantic analysis
- latent dirichlet allocation
- topic models
- text retrieval
- pattern recognition
- retrieval systems
- linear discriminant analysis
- document categorization
- image classification
- text classification
- feature space
- machine learning
- classification accuracy
- decision trees
- dimensionality reduction
- dimension reduction
- support vector machine svm
- structured documents
- document clustering
- latent semantic indexing
- text clustering
- pre classified
- test collection
- metadata
- web documents
- generative model
- co occurrence
- training set
- support vector machine
- information extraction
- latent dirichlet
- feature selection
- boolean queries
- face recognition
- latent topics
- support vector
- digital libraries
- text collections
- classification algorithm
- document representation
- language model
- query expansion
- question answering
- discriminant analysis
- user queries
- query terms