Скачать или смотреть The Engineer's Guide to Text Preprocessing | NLP | 2

Text preprocessing and text representation are fundamental steps in preparing unstructured text data for Natural Language Processing (NLP) models. Text preprocessing involves cleaning and transforming raw text by removing noise such as punctuation, URLs, HTML tags, and by lowercasing words. A crucial technique is stop word removal, which eliminates common words that often carry little meaning, thereby reducing dataset size and potentially improving model performance, though it should be avoided for tasks like machine translation or summarization. Tokenization is the process of breaking down text into smaller units called tokens, which can be words, characters, or subwords, helping machines understand individual meanings and count word frequencies. Furthermore, stemming and lemmatization are text normalization techniques: stemming reduces words to a base form by chopping off endings, which may not be a real word, while lemmatization maps words to their meaningful dictionary root or lemma, often by referring to a dictionary. After preprocessing, feature engineering converts the cleaned text into numerical formats because machine learning and deep learning models cannot directly process raw text. This conversion, known as text representation, includes methods like Bag of Words (BoW). The overall corpus refers to the collection of text documents being analyzed, and Type Token Ratio (TTR) measures lexical diversity based on unique words versus total words in a corpus