Continuous Bag-of-Words Word2Vec, also known as CBOW Word2Vec, is a technique used to create word embeddings that can be used in natural language processing. These embeddings are numerical representations of words, which allow computers to understand their meanings.
What is CBOW Word2Vec?
CBOW Word2Vec is a neural network architecture that uses both past and future words in a sentence to predict the middle word. This technique is called a "continuous bag-of-words" because the order of the wor
FastText: An Overview of Subword-based Word Embeddings
FastText is a type of word embedding that utilizes subword information. Word embeddings are numerical representations of words that allow machines to understand natural language. They help improve the performance of various natural language processing (NLP) tasks, such as sentiment analysis, text classification, and machine translation.
What are Word Embeddings?
Word embeddings are numerical representations of words that capture their me
What are GloVe Embeddings?
GloVe Embeddings are a type of word embedding that represent words as vectors in a high-dimensional space. The vectors capture the meaning of the words by encoding the co-occurrence probability ratio between two words as vector differences.
The technique of using word embeddings has revolutionized the field of Natural Language Processing (NLP) in recent years. GloVe is one of the most popular algorithms for generating word embeddings.
How are GloVe Embeddings calcu
What is lda2vec?
lda2vec is a machine learning algorithm that creates word vectors while also taking into account the topic of the document that the word is from. It combines two popular algorithms: word2vec and Latent Dirichlet Allocation (LDA).
Word2vec is an algorithm used for language modeling, which tries to predict the probability of a word being used in context. It creates a set of word vectors that are representations of words in a high-dimensional space. This means that words similar
Introduction to Mirror-BERT: A Simple Yet Effective Text Encoder
Language is the primary tool humans use to communicate, and it is not surprising that advancements in technology have led to great strides in natural language processing. Pretrained language models like BERT (Bidirectional Encoder Representations from Transformers) have been widely adopted and used to improve language-related tasks like language translation, sentiment analysis, and text classification. However, converting such mod
What are Poincaré Embeddings?
Poincaré Embeddings are a type of machine learning technique that can help computers understand the relationships between different types of data. Specifically, they use hyperbolic geometry to create hierarchical representations of data in the form of embeddings, which can be thought of as compressed versions of the original data.
How Do Poincaré Embeddings Work?
Poincaré Embeddings work by first representing data in the form of vectors, which are sets of number
Have you ever wondered how computers can understand the meaning behind the words we use? Word embeddings, like those created by Skip-gram Word2Vec, provide a way for machines to represent and analyze language in a more meaningful way.
What is Skip-gram Word2Vec?
Skip-gram Word2Vec is a type of neural network architecture that is used to create word embeddings. Word embeddings are numerical representations of words that computers can use to understand and analyze language. In the Skip-gram Wor