![]() This family of tokenization method is called “bag-of-words.”īecause bag-of-words aren’t an order-preserving tokenization method (the tokens generated are understood as a set, not a sequence, and the general structure of the sentences is lost), bag-of-words tend to be used in shallow language processing models rather than in deep learning models. The term “bag” here refers to the fact that we’re dealing with a set of tokens rather than a list or sequence: the tokens have no specific order. ![]() Such a set is called a “bag-of-3-grams” (resp. It may be decomposed as the following set of 2-grams: Consider the sentence: “The cat sat on the mat”. ![]() The same concept may also be applied to characters instead of words. ![]() Word N-grams are groups of N (or fewer) consecutive words that you can extract from a sentence. Understanding N-grams and “bag-of-words”. In the remainder of this section, we will explain these techniques and show concretely how to use them to go from raw text to a Numpy tensor that you can send to a Keras network. In this section we will present two major ones: one-hot encoding of tokens, and token embeddings (typically used exclusively for words, and called “ word embeddings“). There are multiple ways to associate a vector to a token. These vectors, packed into sequence tensors, are what get fed into deep neural networks. All text vectorization processes consist in applying some tokenization scheme, then associating numeric vectors with the generated tokens. “N-grams” are overlapping groups of multiple consecutive words or characters.Ĭollectively, the different units into which you can break down text (words, characters or N-grams) are called “tokens”, and breaking down text into such tokens is called “tokenization”. This can be done in multiple ways:īy segmenting text into words, and transforming each word into a vector.īy segmenting text into characters, and transforming each character into a vector.īy extracting “N-grams” of words or characters, and transforming each N-gram into a vector. Vectorizing text is the process of transforming text into numeric tensors. Like all other neural networks, deep learning models don’t take as input raw text: they only work with numeric tensors. Deep learning for natural language processing is pattern recognition applied to words, sentences, and paragraphs, in much the same way that computer vision is pattern recognition applied to pixels. Keep in mind throughout this article that none of the deep learning models you see truly “understands” text in a human sense, rather, these models are able to map the statistical structure of written language, which is sufficient to solve many simple textual tasks. The deep learning sequence processing models that we’ll introduce can use text to produce a basic form of natural language understanding, sufficient for applications ranging from document classification, sentiment analysis, author identification, or even question answering (in a constrained context). It can be understood either as a sequence of characters, or a sequence of words, albeit it is most common to work at the level of words. Text is one of the most widespread form of sequence data. Just enter code fccchollet into the discount code box at checkout at. In this article, we’ll learn about deep learning models that can process text (understood as sequences of word or sequences of characters), timeseries, and sequence data in general. From Deep Learning with Python by François Chollet
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