Introduction
Natural language processing (NLP) is gaining more popularity in today’s world, especially in deep learning growth. NLP is a branch of artificial intelligence that focuses on reading and extracting useful information from text, as well as supplementary text-based training. The essential activities are voice recognition and synthesis, text analysis, sentiment classification, machine translation, etc.
Understanding Artificial Intelligence (AI)
Artificial intelligence (AI) is a phrase that relates to systems or robotics that resemble cognitive abilities to execute tasks and may iteratively improve themselves depending on the statistics they collect. Artificial intelligence appears in a variety of forms. Here are a few examples:
- Chatbots employ artificial intelligence (AI) to comprehend client concerns better and give more efficient responses.
- AI is used by intelligent assistants to parse crucial information from massive free-text datasets to optimize scheduling.
- Recommendation engines can provide automated TV show recommendations based on consumers’ viewing behavior.
AI is less about a specific form or function and more like a process to grasp and analyze data more effectively. Although visions of high-functioning, human-like robots sweeping the globe bring up images of AI sweeping the globe, the technology isn’t meant to do so. It’s intended to improve people’s abilities and efforts, making it a highly valuable company asset.
What Is Natural Language Processing?
Natural Language Processing allows computers to comprehend human speech (NLP). Behind the scenes, NLP examines the syntactic structure of sentences and the specific meanings of words, then applies algorithms to derive information and provide results. To put it another way, it understands human language and can perform various tasks on its own.
The most well-known examples of NLP in action are virtual assistants like Google Assist, Siri, and Alexa. NLP translates written and spoken words into numbers that machines can understand, such as “Hey Siri, where is the closest convenience store?.
Chatbots are another well-known application of NLP. They assist support teams in resolving issues by automatically interpreting and responding to typical language queries.
NLP includes several subdomains, including natural language understanding (NLU), which relates to machine reading comprehension, and natural language generation (NLG), which may transform data into human words.
Natural language processing allows computers to extract keywords and phrases, interpret the intent of language, translate it to another language, or provide a response.
When composing a mail, proposing to interpret a Facebook post published in a different language, or filtering undesirable advertising emails into your junk folder are just a few examples of frequent apps where you’ve almost certainly encountered NLP without even noticing it. In a nutshell, Natural Language Processing aims to help machines comprehend human language, which is complex, confusing, and vastly varied.
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What are the top four applications of NLP?
Because NLP enables computers to interpret and generate human speech, it has many applications. Here are some of the most common applications of natural language processing:
- Spelling check: The first grammatical checkers were created to detect flaws with punctuation and style. Grammatical issues such as sentence construction, spelling, grammar, punctuation, and contextual errors can now be recognized more effectively thanks to advances in NLP and machine learning.
- Translation: In modern translation systems, both rule-based and machine learning techniques can be used. By recognizing the underlying theme of an input text, generating a word-to-word translation, and adjusting the output according to training data, ML enhances the general interpretation of a sentence or phrase.
- Chatbots: Chatbots are a type of application that enables humans to have natural conversations with machines by posing questions and receiving responses. Chatbots use natural language processing (NLP) and intent recognition to interpret user requests. They then build responses to the comprehended requests according to the type of chatbot (e.g., rule-based, AI-based, or hybrid). Other AI technologies can be added into chatbots to enhance user experience, such as analytics to monitor and identify patterns in users’ speech and quasi components like graphics or maps.
- Data analytics: NLP, along with other AI applications, is improving analytics. To make data-driven decisions in business or study, analytics is the process of extracting insights from data from multiple sources. Because it allows users to extract, classify, and analyze their text or voice, NLP is particularly useful in data analytics.
NLP, AI. What Is the Distinction between them?
Natural Language Processing (NLP) and Artificial Intelligence (AI) are terms that are sometimes used interchangeably, so it’s easy to get your wires crossed while attempting to distinguish between the three.
The first thing to understand is that Natural Language Processing (NLP) is a subset of Artificial Intelligence.
Artificial intelligence (AI) is a broad term that refers to systems that can replicate human intelligence. It comprises systems that mimic cognitive abilities such as problem-solving and learning from examples. This includes a wide range of applications, from self-driving cars to prediction systems.
The study of how computers perceive and synthesize human speech is known as natural language processing (NLP). Natural language processing allows machines to comprehend written or spoken material and perform translation, keyword extraction, subject categorization, and more (NLP).
However, machine learning is necessary to automate these procedures and provide reliable results. Machine learning is nothing but the process of teaching machines how to learn and develop without being explicitly programmed using algorithms.
NLP is used by AI-powered chatbots to interpret what users say and what they mean to accomplish. In contrast, machine learning is used by AI-powered chatbots to offer more correct responses by learning from previous interactions automatically.
Which is better, NLP or AI?
Because all chatbots are AI-based, anyone working on one can freely use the term “artificial intelligence” while discussing their bot. However, asking whether or not your chatbot should support natural language processing is more crucial than appearing arrogant.
Because NLP is difficult to grasp, I exclusively suggest it to salespeople who have experience with chatbots. When creating a chatbot, it’s important to think about what you want your bot to become in the future. Do you believe your current simple concept will evolve into something more complicated in the future? If that’s the case, you’ll want to find a chatbot-building platform that supports natural language processing (NLP), so you can scale up when the time comes.
Conclusion
Artificial Intelligence, Machine Learning, and Natural Language Processing are in high demand and will continue to be so soon. To improve your job prospects, enroll in AIML courses offered by Great Learning. They offer online classes for artificial intelligence that can be studied from anywhere in the world at your leisure. For a brighter future, get AI certificate training from Great Learning.