Making sense of word2vec

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 year.

Doc2vec tutorial

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 unsupervised learning of continuous representations for larger blocks of text, such as sentences, paragraphs or entire documents.

Data streaming in Python: generators, iterators, iterables

There are tools and concepts in computing that are very powerful but potentially confusing to novices. 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

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

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.

Performance Shootout of Nearest Neighbours: Querying

Previous posts explained the whys & whats of nearest-neighbour search, the available OSS libraries and Python wrappers. We converted the English Wikipedia to vector space, to be used as our testing dataset for retrieving “similar articles”. In this post, I finally get to some hard performance numbers.

Asymmetric LDA Priors, Christmas Edition

The end of the year is proving crazy busy as usual, but gensim acquired a cool new feature that I just had to blog about. Ben Trahan sent a patch that allows automatic tuning of Latent Dirichlet Allocation (LDA) hyperparameters in gensim. This means that an optimal, asymmetric alpha can now be trained directly from your data.