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Reason Behind Using GloVe
Key Takeaways
Last Updated: Mar 27, 2024

GloVe Embedding in NLP

Author Mayank Goyal
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The gloVe is an efficient word vector learning algorithm discussed in this post. This article will explain why GloVe is superior and the motivation behind GloVe's cost function, which is the most critical aspect of the algorithm.

The gloVe is a word vector technology that, after a brief pause, rode the wave of word vectors. To refresh our memory, word vectors organize words into a pleasant vector space where related words cluster together, and different words repel each other. The gloVe benefits that, unlike Word2vec, it does not rely solely on local statistics (local context information of terms) to generate word vectors but also incorporates worldwide statistics (word co-occurrence).

Reason Behind Using GloVe

On the premise that "you shall know a word by the company it keeps," word vectors were created. You take a large corpus and turn it into a dataset of tuples, with each tuple containing a single value (some word x, a word in the context of x). Then, given the word x, you'd utilize your old pal, a neural network, to learn to predict the context word of x. Why not stay with Word2vec after its impressive performance? The cause is the fundamentals of the solution formulation, not performance. Keep in mind that Word2vec only uses language information from the local area. The semantics learned for a specific word are only influenced by the words that surround it. 

Take the line "The cat sat on the carpet," for example. If we use Word2vec, we won't be able to find out whether "the" is a particular context for the terms "cat" and "carpet." Is "the" merely a stopword?

This can be considered suboptimal, especially among theoreticians.

This is when GloVe enters the picture. "Global Vectors" is the abbreviation for "Global Vectors." GloVe, as previously indicated, captures both global and local statistics of a corpus to generate word vectors. Do we, however, require both international and local statistics? It turns out that each form of statistic has its benefit. Word2vec, which records local statistics, performs exceptionally well in analogy tasks. However, a method like LSA that uses global statistics performs poorly in analogy tasks. However, because the Word2vec technique relies solely on local statistics, it has several drawbacks (as stated above).

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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.


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


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:



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. 

I hope you all like it.

I hope you all like this article. Want to learn more about Data Analysis? Here is an excellent course that can guide you in learning. Can also refer to our Machine Learning course.

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