Short technical information about Word2Vec, GloVe and Fasttext

Short technical information about Word2Vec, GloVe and Fasttext

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.

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. 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.

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)

word-embeddings nlp glove-making-machine word2vec fasttext

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