Displaying 1 to 11 from 11 results

lda - LDA topic modeling for node.js

  •    Javascript

Latent Dirichlet allocation (LDA) topic modeling in javascript for node.js. LDA is a machine learning algorithm that extracts topics and their related keywords from a collection of documents. In LDA, a document may contain several different topics, each with their own related terms. The algorithm uses a probabilistic model for detecting the number of topics specified and extracting their related keywords. For example, a document may contain topics that could be classified as beach-related and weather-related. The beach topic may contain related words, such as sand, ocean, and water. Similarly, the weather topic may contain related words, such as sun, temperature, and clouds.

trlda - Implementations of various online inference algorithms for LDA, with Python interface.

  •    C++

Additional features include adaptive learning rates (Ranganath et al., 2013) and automatic tuning of hyperparameters via empirical Bayes. I have tested the code with the versions above, but older versions might also work.




hlda - Gibbs sampler for the Hierarchical Latent Dirichlet Allocation topic model

  •    Jupyter

Hierarchical Latent Dirichlet Allocation (hLDA) addresses the problem of learning topic hierarchies from data. The model relies on a non-parametric prior called the nested Chinese restaurant process, which allows for arbitrarily large branching factors and readily accommodates growing data collections. The hLDA model combines this prior with a likelihood that is based on a hierarchical variant of latent Dirichlet allocation.


go-topics - Latent Dirichlet Allocation

  •    Go

A very basic LDA (Latent Dirichlet Allocation) implementation with some convenient utilities.

PartiallyCollapsedLDA - Implementations of various fast parallelized samplers for LDA, including Partially Collapsed LDA, Light LDA, Partially Collapsed Light LDA and a very efficient Polya-Urn LDA

  •    Java

Måns Magnusson, Leif Jonsson, Mattias Villani, and David Broman. (2017). Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models. Journal of Computational and Graphical Statistics. Alexander Terenin, Måns Magnusson, Leif Jonsson, and David Draper. “Polya Urn Latent Dirichlet Allocation: a doubly sparse massively parallel sampler”. Accepted for publication in IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017.

Natural-Language-Processing-Fundamentals - Use Python and NLTK to build out your own text classifiers and solve common NLP problems

  •    Jupyter

If NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems. You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you'll understand how to apply NLP techniques to answer questions as can be used in chatbots. By the end of this course, you'll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The book will easily equip you with the knowledge you need to build applications that interpret human language.