Natural Language Processing: Bridging Humans and Machines -Part 1 (vector-based models and Text Processing)

Author(s): AbhinayaPinreddy

Originally published on Towards AI.

Natural Language Processing: Bridging Humans and Machines -Part 1 (vector-based models and Text Processing)

Natural Language Processing (NLP) is a fascinating field of Artificial Intelligence (AI) that focuses on enabling computers to understand, interpret, and generate human language. It bridges the gap between human communication and computer understanding, allowing machines to interact with us using words — not just numbers or code.

This article delves into the intricacies of Natural Language Processing (NLP), discussing its definition, applications, and various foundational concepts such as tokenization, text processing, vector representations, and word embeddings. It emphasizes the importance of preprocessing in NLP and outlines popular techniques like bag-of-words, count vectorization, and TF-IDF, which aid in transforming raw text into a format suitable for machine learning algorithms. Finally, the discussion touches on vector similarity and neural word embeddings, providing a comprehensive overview of essential elements that contribute to effective NLP applications.

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Published via Towards AI