12— Understanding Glove: Word Embedding in NLP
In this article, we will talk about Glove one of the word embedding techniques. Before we start, I recommend you read the article I have previously explained on Word Embedding.
Glove is one of the word embedding methods. It is an unsupervised learning algorithm introduced in 2014 for obtaining vector representations for words. It generates word embeddings by aggregating the global word-to-word co-occurrence matrix from a corpus. If two words co-occur many times, it means they have some linguistic or semantic similarity.
Glove is an extension to the Word2Vec method. Word2Vec only captures the local context of words. During training, it only considers neighboring words to capture the context. Glove considers the entire corpus and creates a large matrix that can capture the co-occurrence of words within the corpus.
Why Should We Use Glove?
Large Datasets: If you are working with a very large dataset and want to capture global relationships between words, Glove may be a better fit.
Semantic Similarity: If you want to capture semantic similarities between words more accurately, Glove’s reliance on global statistics may be an advantage.
Resource Limitations: If computational power is limited and you are looking for an efficient method for large datasets, Glove may have the advantage of lower computational requirements.
Glove finds great performance in world analogy and named entity recognition problems.
You can begin working with the Glove by downloading the necessary code from the Glove website. The available code is written in both C and Python and comes with tools for training the Glove embedding on your own datasets as well as evaluating the performance of the trained model.
Glove embeddings are a traditional type of word embedding that encodes the co-occurrence probability ratio between two words as vector differences. However, rapid developments in the field of NLP have led to Transformer-based models, especially BERT and GPT, setting a new standard in this field. That’s why I won’t talk about glove issue any further.
Conclusion
In conclusion, Glove embedding is a powerful tool for creating word glove embeddings, which are numerical representations of the meanings of words. Glove embeddings can be applied to NLP tasks like language translation, text classification, and information retrieval. Glove embedding employs a co-occurrence matrix to learn word relationships and can be trained on large datasets to acquire rich and precise embeddings.
