Like Latent Semantic Analysis (LSA) and probabilistic LSA (pLSA) – see my previous post “LSA and pLSA in Python“, Latent Dirichlet Allocation (LDA) is an algorithm which, given a collection of documents and nothing more (no supervision needed), can uncover the “topics” expressed by documents in that collection. LDA can be seen as a Bayesian extension of pLSA.
(more…)
Latent Semantic Analysis (LSA) and its probabilistic counterpart pLSA are two well known techniques in Natural Language Processing that aim to analyze the co-occurrences of terms in a corpus of documents in order to find hidden/latent factors, regarded as topics or concepts. Since the number of topics/concepts is usually greatly inferior to the number of words and since it is not necessary to know the document categories/classes, LSA and pLSA are thus unsupervised dimensionality reduction techniques. Applications include information retrieval, document classification and collaborative filtering.
(more…)
Two years ago, I wrote about a port to Ruby of Universal Encoding Detector, which is itself a port to Python of Mozilla’s character encoding detection algorithm.
Recently being interested in Machine Learning, I read about naive Bayes classifiers. I then remembered the encoding detector program and thought that naive Bayes classifiers would be a good candidate for this kind of problem. Going back to the Universal Encoding Detector’s home page, I found a link to: