Learning about Natural Language Processing is a continuous task, which appears not to have an end, but, there is always a starting point where one learns the problem definitions and the common algorithms to solve them. In this post, I will share some of the books that I used during my path to learn about Natural Language Processing. I think most of these books are a good starting point to learn about Natural Language Processing and how to apply Machine learning to NLP tasks. Most of these books and tutorials are nice to have around so that you can quickly clarify any doubts or review how a certain algorithm or technique works. I personally like to have them at hand :)

A collection of several SPARQL queries to WikiData. Those are queries over different domains or topics that I’ve used for different goals. I’ve just decided to make them here public so that I can quickly refer to and reused them in other queries.

The Attention mechanism is now an established technique in many NLP tasks. I’ve heard about it often but wanted to go a bit more deeply and understand the details. In this first blog post - since I plan to publish a few more blog posts regarding the attention subject - I make an introduction by focusing on the first proposal of attention mechanism, as applied to the task of neural machine translation.

While working on some projects of mine I come to a point where I needed pre-trained word embeddings for Portuguese. I could have trained some on my own on some corpora but I did not want to spend time on cleaning and running the training, so instead I searched the web for collections of word vectors for Portuguese, here’s a compiled list of what I’ve found.

KONVENS is an annual conference, which gathers together the computer scientist and computational linguist community working with the German language.

Since the work of Mikolov et al., 2013 was published and the software package word2vec was made public available a new era in NLP started on which word embeddings, also referred to as word vectors, play a crucial role. Word embeddings can capture many different properties of a word and become the de-facto standard to replace feature engineering in NLP tasks.

Recently (i.e., at the time of this writing since 2015~2016 onwards) new methods to perform sequence labelling tasks based on neural networks started to be proposed/published, I will try in this blog post to do a quick recap of some of these new methods, understanding their architectures and pointing out what each technique brought new or different to the already knew methods.