👉 Readings computing, often associated with the field of natural language processing (NLP), involves transforming textual data into structured numerical representations that machines can process and analyze. This process typically includes tokenization, where text is broken down into smaller units like words or subwords; stemming and lemmatization, which reduce words to their base forms; and vectorization techniques such as Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), or word embeddings like Word2Vec and GloVe. These methods convert text into high-dimensional vectors, capturing semantic relationships and contextual meanings. Advanced models like BERT and GPT further enhance this by using deep learning architectures to generate contextualized embeddings, enabling more nuanced understanding and generation of human-like text. This transformation is crucial for tasks like sentiment analysis, machine translation, and question-answering systems, allowing computers to interpret and generate human language effectively.
