Representing text numerically enables processing by neural networks. Token sizes can vary from individual letters to entire words.
In natural language processing (NLP), transforming text into a numerical representation is a fundamental step that allows neural networks to make sense of and process the data. This transformation is necessary because neural networks operate on numerical data. The process begins with tokenization, which is the act of breaking down text into smaller components known as tokens. Tokens can represent a variety of linguistic units—ranging from individual characters and subwords to entire words and even sentences. The choice of token size can significantly impact the performance and efficiency of the neural network model. For example, when working with languages with rich morphology or compound words, like German or Turkish, using subword tokens can be advantageous. This approach not only captures the meanings effectively but also reduces the vocabulary size, thereby enhancing computational efficiency.
Tokenization can be implemented in various ways, influenced by the specific requirements of the task at hand. Unigram tokenization breaks text into individual words, a technique that can be highly effective for simpler languages with a less diverse range of word forms. On the other hand, Byte Pair Encoding (BPE) merges the most frequent pairs of bytes or characters into subwords iteratively. This is particularly useful for handling out-of-vocabulary words in languages with high lexical diversity. Another common technique is character-level tokenization, which splits text into individual characters, providing granular control but at the expense of larger sequence lengths which may complicate the training of neural networks. Furthermore, sentencepiece is a more sophisticated tokenizer that accommodates the intricacies of multi-lingual text, often used in machine translation and other cross-linguistic applications. Regardless of the tokenization method employed, the key objective remains the same: to create a meaningful numerical representation that allows neural networks to parse, understand, and generate human language with impressive accuracy and coherence.