Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that focuses on the ability of computers to understand, analyze, and process human natural language.<\/p>\n\n\n\n
In today’s digital era, communication between humans and machines has become increasingly important. One of the advanced technologies that enables this interaction is natural language processing (NLP). But what exactly is natural language processing? In this article, we will discuss what NLP is, the concept of natural language processing, as well as various examples and applications of this technology across different fields.<\/p>\n\n\n\n
Natural Language Processing (NLP) is a branch of artificial intelligence (AI) science that focuses on the ability of machines (computers) to understand, analyze, and process human natural language. NLP allows computers to interpret complex human language, which is known for its ambiguity and diverse styles.<\/p>\n\n\n\n
Natural language processing is a technology aimed at bridging communication between humans and machines without the need for complicated programming languages. NLP is often used to build chatbot applications that can automatically respond to user questions. NLP also enables machines to read and understand the content of a document, identify emotions in a text, or even execute voice commands.<\/p>\n\n\n\n
The way Natural Language Processing (NLP) works begins with models trained to perform specific text analysis tasks. These models can learn from vast amounts of language data to understand patterns and context.<\/p>\n\n\n\n
In its current development, many NLP applications utilize complex generative AI models, but simpler NLP language models are often sufficient and more cost-effective for many common text analytics cases.<\/p>\n\n\n\n
To allow computers to easily process text data, a fundamental workflow is required, known as the Text Preprocessing<\/strong> stage. Text preprocessing is the initial step in preparing text data before entering the further analysis phase by the NLP model.<\/p>\n\n\n\n Imagine you are a chef preparing a delicious dish. Before you start cooking, of course, you must wash, cut, and prepare the raw ingredients first. Vegetables must be cleaned, meat sliced, and spices sorted. All this is done so that the cooking process runs smoothly and the results are optimal.<\/p>\n\n\n\n The same applies in Natural Language Processing (NLP). In text preprocessing, there are several main steps that play a crucial role in preparing text data:<\/p>\n\n\n\n Tokenization is the preprocessing process of breaking sentences or paragraphs into smaller parts (tokens). These tokens are usually words or phrases. This process helps the model recognize the smallest units of information.<\/p>\n\n\n\n Lower casing is the process of converting all letters in a text into lowercase. This aims to avoid differences in meaning or calculation caused by letter capitalization. For example, the words “Woman” and “president” will be read as the same information after the lower casing process.<\/p>\n\n\n\n Stopword removal is the process of eliminating common words that frequently appear but carry little meaningful value, such as “the,” “and,” or “or.” This helps reduce noise in the data.<\/p>\n\n\n\n Stemming is the process of reducing words to their root form. For example, “running” becomes “run.” This aims to unify word variations into a single form with the same meaning.<\/p>\n\n\n\n This process aims to remove punctuation marks or non-alphabetic characters like numbers or symbols that do not carry informational value in NLP processing.<\/p>\n\n\n\n
Before text is analyzed by an artificial intelligence model, it must go through several Text Preprocessing<\/strong> stages.<\/p>\n\n\n\nTokenization<\/strong><\/h3>\n\n\n\n
Lower Casing<\/strong><\/h3>\n\n\n\n
Stopword Removal<\/strong><\/h3>\n\n\n\n
Stemming and Lemmatization<\/strong><\/h3>\n\n\n\n
Punctuation & Noise Removal<\/strong><\/h3>\n\n\n\n
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