Natural Language Processing (NLP) is a technology integrated within the broader field of Artificial Intelligence (AI). The overarching goal of NLP is to process high-level human language (such as sentences, paragraphs, or entire documents) through a computer system and transform it into a low-level language that machines can read, understand, and navigate more easily.
NLP includes various analytical techniques grouped under the phase called semantic analysis, which is concerned with understanding the meaning contained within a statement in the text. Key tasks falling under semantic analysis include entity analysis (or Named Entity Recognition, NER), sentiment analysis, and topic modeling. Semantic analysis aims to uncover the definitions of words, phrases, and sentences and verify if the word organization makes semantic sense.
To achieve analysis, NLP tasks typically require text data to be converted into a numerical format through preprocessing steps like tokenization or vectorization (e.g., using word embeddings or transformer embeddings). Many modern NLP APIs, like Google Cloud’s Natural Language API, are highly versatile and are trained to handle various semantic analysis tasks simultaneously, including syntax analysis, content moderation, and classification.