Tokenizáciu

Tokenizáciu is a fundamental process in natural language processing (NLP) and computer science. It involves breaking down a larger piece of text, such as a sentence, paragraph, or document, into smaller units called tokens. These tokens can be words, sub-word units, punctuation marks, or even individual characters, depending on the specific tokenization strategy employed.

The primary goal of tokenization is to transform unstructured text data into a structured format that can be more easily processed and analyzed by machines. Different applications may require different levels of granularity in tokenization. For instance, simple word tokenization might split "hello, world!" into ["hello", ",", "world", "!"]. More advanced methods, like sub-word tokenization, can break down words into smaller meaningful units, which is particularly useful for handling out-of-vocabulary words or morphologically rich languages.

The process typically involves defining rules or using pre-trained models to identify the boundaries between tokens. Whitespace is a common delimiter, but punctuation and specific linguistic patterns also play a crucial role. Tokenization is a prerequisite for many NLP tasks, including text classification, machine translation, sentiment analysis, and information retrieval. The quality of tokenization directly impacts the performance of these downstream applications.