Torkprocessen

Torkprocessen, also known as the Torkenization process, is a method used in the field of natural language processing (NLP) to convert text into a more manageable and standardized format. This process involves several steps, including tokenization, normalization, and lemmatization. Tokenization is the initial step where the text is divided into individual words, phrases, or symbols, known as tokens. Normalization follows, where tokens are converted to a consistent format, such as lowercasing all letters or removing punctuation. Lemmatization is the final step, where tokens are reduced to their base or root form, known as a lemma. This process helps in reducing the dimensionality of the text data, making it easier to analyze and process. Torkprocessen is widely used in various applications, such as text classification, sentiment analysis, and information retrieval, to improve the accuracy and efficiency of these tasks.