The best vector representation of each word is learned during the model training process. The dense word vectors are part of the model parameters. PCA, topic models, and neural probabilistic language models are all good examples of this category.ĭifferent from the count-based approaches, context-based methods build predictive models that directly target at predicting a word given its neighbors.

The models map count-based statistics like co-occurrences between neighboring words down to a small and dense word vectors. Given a local context, we want to design a model to predict the target words and in the meantime, this model learns the efficient word embedding representation.Ĭount-based vector space models heavily rely on the word frequency and co-occurrence matrix with the assumption that words in the same contexts share similar or related semantic meanings. Context-based: The second approach is supervised.Raw co-occurrence counts do not work well, so we want to do smart things on top. Count-based: The first one is unsupervised, based on matrix factorization of a global word co-occurrence matrix.There are two main approaches for learning word embedding, both relying on the contextual knowledge. Contextual information is super useful for learning word meaning and relationship, as similar words may appear in the similar context often. An intuitive assumption for good word embedding is that they can approximate the similarity between words (i.e., “cat” and “kitten” are similar words, and thus they are expected to be close in the reduced vector space) or disclose hidden semantic relationships (i.e., the relationship between “cat” and “kitten” is an analogy to the one between “dog” and “puppy”). Word embedding represents words and phrases in vectors of (non-binary) numeric values with much lower and thus denser dimensions. However, one-hot encoding is impractical computationally when dealing with the entire vocabulary, as the representation demands hundreds of thousands of dimensions. One of the simplest transformation approaches is to do a one-hot encoding in which each distinct word stands for one dimension of the resulting vector and a binary value indicates whether the word presents (1) or not (0). In order to make a machine learning model understand and process the natural language, we need to transform the free-text words into numeric values.

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