Introduction: With the help of Deep Learning, Natural Language Processing (NLP) has evolved quickly. More and more companies are willing to treat texts faster and in large quantities, and this is why NLP is one of the dynamic areas in Artificial Intelligence research.
With the help of Deep Learning, Natural Language Processing (NLP) has evolved quickly. More and more companies are willing to treat texts faster and in large quantities, and this is why NLP is one of the dynamic areas in Artificial Intelligence research. However, these researchs are mainly dedicated to English : until now, most companies handle French by translation, or as one language among others through multi language algorithms. This is all the more critical since Deep Learning can lead to more accurate but less interpretable results.
Through this article, I try to give insights about the main differences between three of the most famous embeddings.
Word Embedding makes words “understandable” by machines. Its main goal is to capture a type of relationship between words. This relationship could be morphological, semantic, contextual, or syntactic for example. A quick way to translate words into vectors would be to convert all words into integers and then take these integers as indices for their one-hot encoding.
Let consider a one hot encoding for three sentences : “_Dog and cat play_”, “_Dog eat meat_”, “_Dog and cat eat_”. Here, we could give integers to all the words present in these sentences, by order of apparition, and the length of the One hot encoding would be the length of the set of words. We have there 6 unique words, the length of the vectors of the One-Hot Encoding is 6.
• Dog (1, 0, 0, 0, 0, 0),
• and (0, 1, 0, 0, 0, 0),
• cat (0, 0, 1, 0, 0, 0),
• play (0, 0, 0, 1, 0, 0),
• eat (0, 0, 0, 0, 1, 0),
• meat (0, 0, 0, 0, 0, 1)
In our previous discussion we had understand the basics of tokenizers step by step. If you had not gone through my previous post i highly recommend just have a look at that post because to understand Embeddings first, we need to understand tokenizers and this post is the continuation of the previous post. I am providing the link below of my post on Tokenizers. I had explained the concepts step by step with a simple example Understanding N
One hot encoding usually works in some situations but breaks down when we have a large vocabulary to deal with because the size of our word representation grows with the number of words. What we need is a way to control the size of our word representation by limiting it to a fixed size vector. There comes the need for word embeddings!
English NLP, and non-English NLP applications in particular, can often gain up to a 10% boost in classifier accuracy by upgrading to high-quality word embeddings.
Provides a gentle introduction to word embeddings and dives into implementation details using Tensorflow. Starting with the basic foundations of word embeddings, we’ll gradually explore the depths as we advance through the article.
Explaining the concepts and use of word embeddings in NLP, in text classification. In this blog post we are going to explain the concepts and use of word embeddings in NLP, using Glove as en example.