Word Tokenization
Word tokenization is a fundamental natural language processing (NLP) technique that involves splitting text into individual words or tokens. It serves as a preprocessing step for tasks like text analysis, machine translation, and sentiment analysis by breaking down raw text into manageable units. This process often handles punctuation, contractions, and language-specific rules to accurately segment text.
Developers should learn word tokenization when working on NLP projects, such as building chatbots, search engines, or text classification systems, as it's essential for converting unstructured text into structured data. It's particularly crucial for languages with complex word boundaries (e.g., Chinese or German compounds) and for applications like information retrieval where precise word separation improves accuracy. Mastering this skill enables efficient text processing and forms the basis for more advanced NLP techniques like stemming or part-of-speech tagging.