Natural Language Processing and its uses
Natural Language Processing (NLP) is the ability of machines to understand and interpret human language via text or voice. The objective of NLP is to make a computer or machine as intelligent as human beings in understanding languages. It is a combination of natural language understanding and natural language generation.
Natural Language Understanding (NLU): NLU is used to identify the problem/input given by the user. It analyses and understands the data based on the text to give useful representations.
Natural Language Generation (NLG): Meaningful phrases and sentences need to be produced for proper user responses. NLG is used to transform the data into plain text. It produces sentences or phrased text for the user.
NLP is important to make computers understand natural language, but it is not an easy mission. Computers understand the spreadsheets and database tables which are structured, but unstructured data like human languages, text, and voice is difficult to understand. NLP is used in machine translation, speech recognition, sentiment analysis, and so on by analyzing text and allowing machines to understand how humans communicate.
Automatic Summarization: It is the process of creating a short, accurate, and fluent summary of a large text document. The most important advantage of using this is, it greatly reduces the reading time.
Machine Translation: Machine Translation is the process of converting the text in one language to another automatically. This is used to develop computer algorithms that allow automatic translation without any human intervention, such as Google Translate for example.
Sentiment Analysis: Sentiment analysis is a machine learning technique that identifies polarity (positive or negative opinion) within the text of a whole document, paragraph, or sentence. Tools used in sentiment analysis allow businesses to recognize customer sentiment in online reviews towards products, brands, and services.
Chatbots: A chatbot is a computer program that mimics human conversation through voice or texts. Chatbots are also known as conversational agents that use AI and NLP to understand human intention and perform the actions accordingly.
Spell Checking: A spell checker is a tool that identifies and corrects spelling mistakes in any given text. Grammarly is a fine example. It is an online grammar checker, which captures all types of mistakes, including typographical errors, sentence construction of the text, and so on.
Speech Recognition: Speech recognition is the process of capturing spoken words and converting them into text-like words. A voice assistant uses NLU and speech recognition to understand the user’s intention and performs the actions accordingly. Popular examples of voice assistants include Google Assistant, Alexa, and Apple Siri.
Apple Siri works on two key technologies – speech recognition and NLP. When we invoke Siri by saying “Hey, Siri”, speech recognition is triggered and converted into text. There are numerous difficulties involved, such as the voice match of two individual persons, slang, and phrases. Slang and phrases vary among different cities, states, and countries. Once the text is understood, NLP comes into play and the converted text is sent to the apple servers for further processing. The servers then run NLP algorithms to understand the user’s intention.
For example, when we say “Siri, call John” it must be understood that a call must be made, and it must be made to that person (John) only. This is quite a challenge because not all people use the same sentence to convey a meaning.
Me: Hey Siri, can you make a call?
Siri: Who would you like to call?
Me: To John
In the above scenario, Siri can understand and correlate that John is the continuation of the last message where the user asked to make a call. That part is handled by ML, which is the most important technology behind Siri to understand the contextual flow/conversation.
Another technology used in Siri is Entity Extraction. When you ask Siri to make a call to John, it not only understands the sentence but also identifies the keywords called entities such as “John” and “Make a call”.
Much like Apple’s Siri, Google Assistant also works on AI concepts such as NLP and ML to take input from the user to give the desired output. Google’s assistant is invoked by saying “Hey Google” or “OK Google”. Google Assistant can play music, give suggestions, give weather forecasts, schedule appointments, and even make calls.
Difference between AI, NLP, and ML:
Artificial intelligence (AI)
AI is used to make computers smarter and making them perform human activities. Voice assistants and chatbots are the best examples.
Consider a scenario where a user asks a bot to book a flight from Chennai to Hyderabad on a particular date. The bot identifies that the user intends to book a flight ticket for that date and destination and seeks confirmation from the user. If the user says yes, it will display the information of all the available flights.
Machine Learning (ML)
ML is sometimes synonymously used with AI but it is a subset of AI. Without programming and based on trained data, it implements an ML model. The most important feature of ML is self-learning, which requires a lot of data to perform the tasks.
ML includes various approaches – supervised learning, unsupervised learning, and reinforcement learning.
In supervised learning, a system learns based on the training data, and based on the relation between input and output, it provides the preferred output. Unsupervised learning means acting without any guidance or directions. The machine must figure out based on the detected patterns and styles. Reinforcement learning establishes a pattern behavior and produces actions to discover errors or rewards. The agent has to try all possible actions to learn about its environment to get rewards. Maze games are the best examples of reinforcement learning.
Natural Language Processing (NLP)
We can grant access to machines, but the only problem with this strategy is we need to make our environment by developing advanced code readable for computers. This is time-consuming and impractical to implement the entire human experience in AI. By using NLP, machines can easily understand the written and spoken language.
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