What is Natural Language Processing?

Natural language processing is a branch of computer science and, more specifically, a branch of Artificial Intelligence. A computer program can recognize and understand spoken and written language. This is why it is called natural language. Computer science and computational linguistics are among the disciplines incorporated into natural language processing to close the gap between human and computer communication.

Examples of Natural Language Processing

Artificial Intelligence and natural language processing might conjure up images of futuristic robots. Still, there are already basic examples of natural language processing in our daily lives. Here are a few notable examples:

• Spell check
• Autocomplete
• Voice text messaging
• Spam filters
• Related keywords on search engines
• Siri, Alexa, or Google Assistant

Using natural language processing, machines can also parse and interpret human speech. It can be found in translation software, chatbots, grammar correction software, and social media monitoring software.

Why is Natural Language Processing important?

As computer technology evolves beyond its artificial constraints, organizations seek new ways to take advantage of it. Recent increases in computing speed and capabilities have led to the development of new and brilliant software systems, some of which may eventually replace or augment human services. 

One of the best examples is natural language processing, with intelligent chatbots poised to revolutionize customer service.

In addition to enabling ambiguity resolution in language, natural language processing adds functional numerical structure to the data for downstream applications, such as speech recognition and text analytics.

Natural language processing makes it possible for businesses to detect and process massive volumes of text data across the digital world, including social media platforms, online reviews, news reports, etc.

Natural language processing also provides businesses with valuable insights into brand performance by collecting and analyzing business data. Furthermore, natural language processing models can detect persisting issues and take necessary mitigation measures to improve performance.

Natural Language Processing vs. Other Technologies & Methodologies

Natural Language Processing vs. Machine learning

Artificial Intelligence, natural language processing, and Machine Learning are sometimes used interchangeably. The first thing to know is that natural language processing and machine learning are subsets of artificial intelligence.

Artificial Intelligence refers to machines that can simulate human intelligence. An artificial Intelligence system mimics cognitive capabilities, such as learning from examples and solving problems. A wide range of applications is covered, including self-driving cars and predictive systems.

Natural language processing involves computers understanding and translating human language. This enables machines to understand written or spoken text and perform tasks like translation, keyword extraction, topic classification, etc.

To automate these processes and deliver accurate responses, you need machine learning. The process of machine learning involves applying algorithms that allow machines to learn and improve from experience automatically without being explicitly programmed.

Chatbots powered by artificial intelligence, for example, interpret what users say and what they intend to do through natural language processing. They also learn from prior interactions to deliver more accurate responses.

How do they differ? Natural language processing interprets written language, while Machine Learning makes predictions based on patterns learned through experience. 

Natural language processing is part of everyday life, and it is essential to our lives at home and work. We send voice commands to our virtual home assistants, smartphones, and even vehicles without giving it much thought. Voice-enabled applications such as Alexa, Siri, and Google Assistant use natural language processing and machine learning to answer our questions, add activities to our calendars and call the contacts we state in our voice commands. Natural language processing is making our lives easier and revolutionizing how we work, live, and play.

Natural Language Processing vs. Natural Language Understanding

Natural language Understanding is a branch of Artificial Intelligence that uses computer software to understand input in written or spoken sentences. However, Natural language understanding enables human-computer interaction. 

Alternatively, natural language processing processes text literally, like what was said. On the other hand, natural language processing aims to extract the intent and context of the text.

What are the key differences between the two?

Natural language processing is converting unstructured language data into a structured format so that machines can understand speech and text and formulate appropriate responses. Among its subtopics are natural language processing and natural language generation.

In natural language Understanding, a device identifies the intended meaning of a word based on its grammar and context.

The meaning of what is written or said is not always the same. Mistakes and errors may occur. However, natural language understanding ensures that it will infer real intent and meaning even when data is spoken or written with some errors. In other words, it understands the text.

On the other hand, if we talk about natural language processing, how the machine processes the given data. Such as making decisions, taking actions, and responding to the system. It includes the entire end-to-end process. Each time this happens, natural language processing does not require natural language understanding.

Natural Language Processing vs. Text Mining

The purpose for which natural language processing and text mining are utilized is different. In natural language processing, text, speech, or grammatical syntax is analyzed to understand human language. Text mining is the process of extracting information from unstructured and structured content. The approach emphasizes structure rather than meaning.

What are the key differences between the two?

Text Mining:

It seeks to extract significant numerical indices from a text. The textual content should be made available to a range of algorithms. Summaries can be generated from textual content. In essence, it is an artificial intelligence technology for processing information from various textual content documents. To analyze the text effectively, deep learning algorithms are used. The data is saved in an unstructured format.

Natural Language Processing:

It enables computers to recognize natural language. The computer can recognize the structure of a structured class of information, such as spreadsheets and tables. However, human languages, texts, and voices are unstructured, and it is difficult for the computer to recognize them, which is why natural language processing is needed.