What is Natural Language Processing? NLP is simply a computer’s ability to understand ‘natural language’ as we do. Human interaction has a lot of nuances, and sometimes phrases have multiple meanings. Socialization has nurtured humans to understand social cues to the point where our understanding of them is subconscious. For a machine, these social cues fly right past them. NLP strives to fix this. NLP is an offset of Artificial Intelligence (AI) that helps the computer understand human language. The concept of NLP originates from the 1950s where data scientists and linguists utilized rule-based methods to build NLP systems. It included word/sentence analysis, question-answering, and machine translation—mathematicians proficient in algorithms built these NLP systems.
Currently, the neural network-based NLP (referred to as ‘neural NLP’) framework has moved forward in leaps and bounds and has become the governing approach for NLP. Deep learning (Deep learning delves into raw data and learns its attributes) makes tasks such as Machine Translation (MT), machine reading comprehension (MRC), chatbot, etc., so much simpler.
NLP makes AI perform better. AI overall has captured the attention of many businesses. AI gives insights to their business goals and accurately applies the same practically. For example, specific suggestions provided by artificial intelligence can help companies make better decisions faster. Many features and functions of AI can reduce costs, reduce risks, speed up time to market: the list is endless. High-performance, affordable computing power is readily available. The abundance of elemental computing power in the cloud allows easy access to cheap high-performance computing power. Colossal amounts of data are available to train AI accurately. The emergence of various tools for labeling data and processing structured and unstructured data has made the star of the current market.
How does NLP work?
Just as humans have the brain to process natural language, NLP uses AI to take real-world input, process it, and re-route the information in a way a computer can understand. Just as humans have sensory organs like ears and eyes to hear and see, machines have programs for reading and microphones to collect audio. There are two main aspects to natural language processing: data preprocessing and algorithm development.
Data preprocessing involves preparing and scrubbing text data for machines to be able to inspect it. Preprocessing rearranges and pinpoints features in the text that an algorithm can use. A user can achieve this in several ways:
- Tokenization: This is when a user breaks down text into smaller parts.
- Stop word removal: This process removes unwanted or common words and focuses on unique words to better understand the statement.
- Lemmatization and stemming: This process converts words into their root word for easier processing.
- Part-of-speech tagging: This process categorizes speech into nouns, verbs, adjectives, etc.
Now that the user has categorized the entire dataset, they can create an algorithm to process the data.
What is AI?
Alan Turing, also known as the 'father of computer science,' defines Artificial Intelligence (AI) as "systems that act like humans." AI is a machine that can perform chores that typically require human intelligence. It is a machine that thinks and acts humanely, rationally. Patrick Winston, professor of artificial intelligence and computer science at MIT, defines AI as "algorithms enabled by constraints, exposed by representations that support models targeted at loops that tie thinking, perception and action together." At its core, AI combines computer science and robust datasets to enable problem-solving. It also embraces sub-fields of Natural Language processing, machine learning, and deep learning, frequently mentioned in synchrony with AI.
AI advances by consuming astronomical amounts of data and comprehending the data for correlations and patterns. It uses these patterns to make a prognosis. For instance, an algorithm feeds chatbots prototypes of text chats that help them determine how humans communicate with each other. Similarly, image recognition tools can learn to unearth and classify objects in images by reviewing a surplus of data. AI focuses on three cognitive skills: learning, reasoning, and self-correction.
AI is a machine that uses subsets of technology like machine learning, deep learning, natural language processing to produce the optimum solution to the task provided. NLP helps bridge the gap between human interaction and machine understanding which improves the overall quality of AI. AI can hold simple and limited conversations like when one asks their google assistant a question, it uses the internet to answer. Though the answer is theoretically correct, it lacks a human touch. As NLP and AI progress, users will accumulate more trained data, eventually leading to a stage where the AI can handle a conversation without human interference.