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In recent years, the way computers understand human language has improved a lot, thanks to a technique called word embeddings. These embeddings help machines grasp the meanings of words by representing them as mathematical vectors. Two of the most popular models for creating word embeddings are Word2Vec and GloVe, each playing a significant role in the field of Natural Language Processing (NLP).
What Are Word Embeddings?
Word embeddings are special ways to represent words as numbers in a multi-dimensional space. This means that words with similar meanings or that are used in similar contexts are placed closer together in this space. For example, the words king and queen might have similar representations because they are both related to royalty. By using word embeddings, computers can better understand the relationships between words, which is essential for tasks like translation, sentiment analysis, and chatbots.
Word2Vec was introduced by a team at Google led by Tomas Mikolov in 2013. This model changed the game by focusing on how words are used in context. Word2Vec works in two main ways:
a. Continuous Bag of Words (CBOW): This model predicts a target word based on the context of the surrounding words in a sentence or text. For example, given the words "the cat on the," CBOW would predict "mat."
b. Skip-Gram: It is used to predict the context word for a given target word. It's reverse of CBOW algorithm. For instance, if the target word is "cat," it would try to predict words like "the," "on," and "mat."
Word2Vec is very fast to train because it creates vectors very efficiently which means that with a large input of text it can create high quality word embeddings. The enthusiasm for this model has made it a cornerstone of many applications in NLP, including better search and recommendations.
While Word2Vec focuses on local context, GloVe (Global Vectors for Word Representation), developed by researchers at Stanford in 2014, looks at the bigger picture. GloVe uses a global approach by examining how often words appear together across an entire dataset.
GloVe produces the word vectors from a co-occurrence matrix that records the number of times two words have occurred in the same window. Through this way, GloVe results in learning the relationships between words in a way that was less realized by other methods. They have demonstrated especially when a general sense of given words is required as well as in the procedures that refer to lengths.
Recently, new models have emerged that enhance word embeddings significantly. Models such as ELMo, BERT, and GPT generate contextualized embeddings, which means that the representation of a word varies based on the words around it.
For instance, the word "bank" has distinct meanings in the phrases "He sat by the bank of the river" and "He went to the bank to deposit money." Contextualized models like BERT consider the entire sentence to grasp the meaning more effectively. Introduced by Google in 2018, BERT employs a transformer architecture to facilitate a deeper understanding of language.
For example, the word "bank" has different meanings in "He sat by the bank of the river" and "He went to the bank to deposit money." Contextualized models like BERT consider the entire sentence to understand the meaning better. BERT, introduced by Google in 2018, uses a transformer architecture to achieve this, allowing for a deeper understanding of language.
The evolution of word embeddings has revolutionized the way machines interpret human language, beginning with Word2Vec and GloVe and progressing to more sophisticated contextualized models. These developments enhance computer language comprehension and facilitate the creation of applications that foster more natural interactions between humans and machines. As researchers keep pushing the boundaries, the future of word embeddings appears promising, paving the way for advancements in technology across education, healthcare, and other fields.
