GloVe Embedding in NLP

GloVe

Various word embedding approaches play a role in capturing the meaning, semantic relationship, and context of different words when creating representations of words. A word embedding is a technique for making dense vector representations of words that include some context terms. These are enhanced versions of simple bag-of-words models such as word counts and frequency counters, typically used to describe sparse vectors.

Word embeddings are based on a vast text corpus and employ an algorithm to train fixed-length dense and continuous-valued vectors. Each word is a point in vector space learned and moved around the target word while semantic links are preserved. The vector space representation of words generates a projection in which words with similar meanings are grouped.

One of the significant ways that have led to many remarkable results on deep neural networks with challenges like neural machine translations is the usage of embeddings over other text representation techniques like one-hot encodes, TF-IDF, and Bag-of-Words. Furthermore, some word embedding methods, such as GloVe and word2vec, are likely to approach the same level of performance as neural networks.

For word representation, GloVe stands for Global Vectors. It is a Stanford University-developed unsupervised learning system that aims to construct word embeddings by aggregating global word co-occurrence matrices from a corpus.

The primary idea behind the GloVe word embedding is to use statistics to derive the link between the words. Unlike the occurrence matrix, the co-occurrence matrix informs you how frequently a specific word pair appears together. Each value represents a pair of words that occur together in the co-occurrence matrix.

Example

Consider the following matrix as an example of how to understand the co-occurrence matrix:

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The matrix was created by combining the unique words from two corpora (blocks 1 and 2) into a single matrix. Move vertically from the word cat; there is no duplication of the phrase cat in block 1, which is true in block 2. Moving on to the next pair cat-fast, it has occurred once in both leagues together and twice in the supplied corpus. Take one more pair, cat-the, and count how many times 'the' has appeared with the cat, which is three. The entire matrix is created in the same way. When you compute the ratio of probabilities between two pairs of words, say (cat/fast) = one and (cat/the)=0.5, the result is 2, indicating that 'fast' is more relevant than 'the.' The GloVe pre-trained word embeddings are based on the following principle:

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FAQs

1. What's the difference between GloVe and word2vec embedding?
Texts are used as training data for a neural network by Word2Vec. The embedding that results determines whether words appear in similar contexts. The gloVe looks for word co-occurrences over the entire corpus. Its embeddings are based on the likelihood of two words appearing together.

2. Is GloVe more efficient than Word2Vec?
Word2Vec uses negative sampling in practice by transforming the softmax function to a sigmoid function. The word2vec conversion produces cone-shaped clusters of words in the vector space, whereas the GloVe word vectors are more discrete, making the word2vec calculation faster than the GloVe.

3. Is the GloVe neural network a real thing?
GlobalVectors is a well-known model that learns vectors or words from their co-occurrence information (GloVe). The gloVe is a count-based model, whereas word2vec is a predictive model – a feed-forward neural network that learns vectors to increase prediction abilities.

4. What exactly is GloVe NLP?
The gloVe is an unsupervised learning technique that generates word vector representations. The resulting representations highlight intriguing linear substructures of the word vector space, and training is based on aggregated global word-word co-occurrence statistics from a corpus.

Key Takeaways

In this article, we've seen how vector representation techniques like GloVe can be utilized to represent a corpus with semantic meaning in this post. We've also seen how GloVe considers a specific term over others based on probabilities and the primary working notion of the GloVe, which is a co-occurrence matrix. That's the end of the article. 

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