If you use ROUGE Evaluation metric for text summarization systems or machine translation systems, you must have noticed that there are many versions of them. So how to get it work with your own systems with Python? What packages are helpful? In this post, I will give some ideas based on engineering’s view (which means…Read more »
Introduction First introduced by Mikolov 1 in 2013, the word2vec is to learn distributed representations (word embeddings) when applying neural network. It is based on the distributed hypothesis that words occur in similar contexts (neighboring words) tend to have similar meanings. Two models here: cbow ( continuous bag of words) where we use a…Read more »
We will focus on POS tagging in this blog. Notations While HMM gives us a joint probability on tags and words: . Tags t and words w are one-to-one mapping, so in the series, they share the same length.
It is somehow a little bit fast to start MT. Anyway, this blog is very superficial, giving you a view on basics, along with an implementation but a bad result…which gives you more chances to optimize. Btw, you might learn some Chinese here 😛
After HMMs, let’s work on a Trigram HMM directly on texts.First will introduce the model, then pieces of code for practicing. But not going to give a full solution as the course is still going every year, find out more in references.
Lecture notes from Natural Language Processing (by Michael Collins)