Topic Modelling with Latent Dirichlet Allocation: How to pre-process data and tune your model. New tutorial.

Ólavur Mortensen gensim, Machine Learning, Open Source, programming, Student Incubator If you’ve learned how to train topic models in Gensim, but aren’t able to get satisfying results, then we have a new tutorial that will help you get on the right track on GitHub. Primarily, you will learn some things about pre-processing text data for the LDA model. You will also get some tips about how …

Author-topic models: why I am working on a new implementation

Ólavur Mortensen gensim, Machine Learning, Open Source, programming, Student Incubator Author-topic models promise to give data scientists a tool to simultaneously gain insight about authorship and content in terms of latent topics. The model is closely related to Latent Dirichlet Allocation (LDA). Basically, each author can be associated with multiple documents, and each document can be attributed to multiple authors. The model learns topic representations for …

What is Topic Coherence?

Devashish Deshpande gensim, programming, Student Incubator 3 Comments What exactly is this topic coherence pipeline thing? Why is it even important? Moreover, what is the advantage of having this pipeline at all? In this post I will look to answer those questions in an as non-technical language as possible. This is meant for the general reader as much as a technical one so I …

2016 Student Data Science Programs with RaRe Technologies

Chris Lakatos gensim, Machine Learning, programming, Student Incubator 2 Comments RaRe Technologies is deeply rooted in the open source community and we are always seeking out opportunities to dedicate our experience and time to the next generation of computer scientists. Often the first step is to connect ambitious students to the resources they need to truly make an impact with hands-on projects and mentorship. These up …

Text Summarization with Gensim

Ólavur Mortensen programming 23 Comments Text summarization is one of the newest and most exciting fields in NLP, allowing for developers to quickly find meaning and extract key words and phrases from documents. RaRe Technologies’ newest intern, Ólavur Mortensen, walks the user through text summarization features in Gensim.

Making sense of word2vec

Radim Řehůřek gensim, programming 50 Comments One year ago, Tomáš Mikolov (together with his colleagues at Google) made some ripples by releasing word2vec, an unsupervised algorithm for learning the meaning behind words. In this blog post, I’ll evaluate some extensions that have appeared over the past year, including GloVe and matrix factorization via SVD.

Doc2vec tutorial

Radim Řehůřek gensim, programming 89 Comments The latest gensim release of 0.10.3 has a new class named Doc2Vec. All credit for this class, which is an implementation of Quoc Le & Tomáš Mikolov: “Distributed Representations of Sentences and Documents”, as well as for this tutorial, goes to the illustrious Tim Emerick. Doc2vec (aka paragraph2vec, aka sentence embeddings) modifies the word2vec algorithm to …

Data streaming in Python: generators, iterators, iterables

Radim Řehůřek gensim, programming 18 Comments There are tools and concepts in computing that are very powerful but potentially confusing even to advanced users. One such concept is data streaming (aka lazy evaluation), which can be realized neatly and natively in Python. Do you know when and how to use generators, iterators and iterables?

Tutorial on Mallet in Python

Radim Řehůřek gensim, programming 32 Comments MALLET, “MAchine Learning for LanguagE Toolkit” is a brilliant software tool. Unlike gensim, “topic modelling for humans”, which uses Python, MALLET is written in Java and spells “topic modeling” with a single “l”. Dandy.

Word2vec Tutorial

Radim Řehůřek gensim, programming 158 Comments I never got round to writing a tutorial on how to use word2vec in gensim. It’s simple enough and the API docs are straightforward, but I know some people prefer more verbose formats. Let this post be a tutorial and a reference example.