What is Word2Vec?
Word2Vec is a technique in natural language processing (NLP) that provides vector representations of words. These vectors capture the semantic and syntactic qualities of words, and their usage in context. The Word2Vec algorithm estimates these representations by modeling text in a large corpus.
Word2Vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2Vec takes as its input a large corpus of text and produces a vector space, typically of several hundred dimensions. Each unique word in the corpus is assigned a vector in the space.
Word2Vec uses a simple neural network with a single hidden layer. During training, the goal is to adjust the weights of the network to reduce a loss function. The computed word vectors (in the hidden layer) for these words have to be similar, thus Word2Vec is motivated to learn similar word vectors for words in similar context.
Word2Vec is able to capture multiple different degrees of similarity between words, such that semantic and syntactic patterns can be reproduced using vector arithmetic. Patterns such as “Man is to Woman as Brother is to Sister” can be generated through algebraic operations.
Word2Vec is not a singular algorithm, rather, it is a family of model architectures and optimizations that can be used to learn word embeddings from large text corpora. Two of the most common methods for learning representations of words are the Continuous Bag-of-Words model, which predicts the middle word based on surrounding context words, and the Skip-Gram model, which uses the current word to predict the surrounding context words.
One of the biggest challenges with Word2Vec is how to handle unknown or out-of-vocabulary (OOV) words. If the Word2Vec model has not encountered a particular word before, it will be forced to use a random vector, which is generally far from its ideal representation.
Word2Vec has a variety of applications, such as text similarity, recommendation systems, and sentiment analysis. It's also used in search engines to improve the accuracy of search results. When a user enters a question, the search engine uses Word2Vec to transform it into a vector representation. This vector is then compared to the representations of documents or web pages to determine which are most relevant.
How does word2vec differ from other techniques for word representation?
Word2Vec is a word embedding technique that uses a shallow, two-layer neural network to learn vector representations of words from a large corpus of text. It differs from other word representation techniques in several ways:
Contextual Understanding — Unlike One Hot Encoding and TF-IDF methods, Word2Vec uses an unsupervised learning process to understand the context of words. It scans the entire corpus and creates vectors based on which words the target word occurs with, revealing the semantic closeness of words to each other.
Vector Representation — In contrast to the Bag-of-Words (BoW) model, Word2Vec represents each word as a vector in a finite dimensional embedding space, rather than representing each sentence as an entity. The weights for each dimension of a word's vector are determined by the word's context, i.e., the neighboring words.
Semantic and Syntactic Similarity — Word2Vec uses the cosine similarity metric to find similarities among words. It assigns similar vector representations to similar words. If the cosine angle is 1, that means words are overlapping. If the cosine angle is 90, that means words are independent or hold no contextual similarity.
Neural Network-Based Variants — Word2Vec offers two neural network-based variants: Continuous Bag of Words (CBOW) and Skip-gram. In CBOW, the neural network model takes various words as input and predicts the target word that is closely related. On the other hand, Skip-gram uses the current word to predict the surrounding context words.
Comparison with GloVe — GloVe (Global Vectors for Word Representation) is another word embedding technique that combines the advantages of two-word vector learning methods: matrix factorization like latent semantic analysis (LSA) and local context window method like Skip-gram. GloVe performs significantly better in word analogy and named entity recognition problems, and it is considered better than Word2Vec in some tasks.
Comparison with Contextual Embeddings — Traditional word embeddings like Word2Vec and GloVe provide a single vector representation for each word, regardless of the context. In contrast, contextual embeddings (e.g., ELMo, BERT) generate different vector representations for a word based on its context, capturing the meaning of a word in different usage scenarios.