What Is Natural Language Processing? (Definition, Uses) | Built In

Natural language processing (NLP) is particularly useful in helping AI understand language contextually. As a result, data extraction from text-based documents becomes feasible, as does facilitating complex analytics processes such as sentiment analysis, voice recognition, topic modeling, entity recognition and chatbots.

Natural language processing is used to facilitate analysis of unstructured data like text from emails and other documents, or voice data collected through products like Amazon Alexa and Google Home.

A vast amount of collected unstructured data is formatted as text-based documents, like emails, transcribed speeches and more. Natural language processing helps computational systems like artificial intelligence understand how this data should be analyzed and categorized, streamlining the amount of human intervention required to discern useful information from the data. While it could take months (or years) for a human to analyze hundreds of documents or gigabytes of sound, it might take a machine mere minutes or hours.

NLP has advanced over the years, resulting in a plethora of coding libraries and pre-trained models that can be applied to virtually any language processing task. Some of the popular models include BERT, GPT-3, Universal Sentence Encoder and word2vec. Today most machines can consistently analyze text-based data better than humans. Compounded by the fact that the human language is massively complex and consists of a wide variety of spoken languages, dialects, syntaxes, grammatical rules, regional terms, slang, semantics, abbreviations and even errors like omitted punctuation and misspellings, natural language processing fills a critical need within data analysis.

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Both are forms of artificial intelligence, but NLP interprets text-based data for context and further analysis, while machine learning makes predictions based on data fed to models for training.

Machine learning makes predictions about future outcomes based on the inputs provided to the model. Machine learning models must be trained through supervised, unsupervised or reinforced learning in order to make predictions that are accurate and usable. These predictions help uncover trends in data that translate to actionable insights.

On the other hand, NLP transforms language into numerical representations used by machine learning algorithms. For example, machine learning performs mathematical operations on numerical data to find relationships and produce predictions. Since text isn’t numeric, the mathematical operations required for machine learning cannot be performed until we transform the text. The challenge for NLP then becomes finding a way to maintain the meaning of the language while representing it in numerical format. Researchers have found that transforming words into vectors of numbers works well for encoding the meaning within the language, producing state-of-the-art results in language modeling tasks. Natural language processing is responsible for powering speech recognition technology in devices such as home smart speakers.

Some of the most common use cases for natural language processing include sentiment analysis, topic modeling, text extraction, chatbots and virtual assistants.

Natural language processing is one of the most powerful tools in existence when it comes to data analysis and how humans communicate with machines. NLP has been woven into daily life for consumers, professionals and businesses.

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Sentiment analysis uses NLP to judge whether language reflects positive or negative emotions. For example, a stock trader might use sentiment analysis to analyze what people are saying and how they feel about a company online. If overall sentiment is good, the trader might buy shares in the company. Some of the largest investment companies in the world monitor social media sentiment to get a feel for how traders might act in the market.

Topic modeling uses NLP to analyze a text corpus and summarize it, breaking it down into relevant topics. This is a common task performed when mining large bodies of text. Topic modeling can reduce volumes of text down to a list of topics, revealing semantic structures that are difficult for humans to detect.

Text extraction is a commonly used method of natural language processing that automatically detects specific information within text, known as named entity recognition. Named entity recognition can be used to pull keywords, names, places, companies and specific phrases from large batches of data to determine trends and find useful insights. It can also be used to streamline operations by setting up automatic triggers that enter pulled data into databases or pull specific customer data in customer service.

Chatbots and virtual assistants use NLP to automatically understand and deliver appropriate answers to user queries through natural language generation that follows pre-defined rules. Today, chatbots are used to answer 80 percent of routine customer questions on average.

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