Natural Language Processing: A Breakthrough Technology In AI
Natural language processing (NLP) is a branch of artificial intelligence that deals with computers understanding and analyzing human speech. NLP is used in data science training and analytics for document classification, sentiment analysis, and social media monitoring. NPL is a crucial module that is important for learning data science.
Using NLP algorithms, computers can be trained to process and parse text to extract meaning. This understanding allows computers to interact with humans more naturally by responding to questions and commands like humans do—using natural language.
The importance of NLP in data analytics comes from the fact that most data is not structured or organized in a way that machines can easily read. For example, suppose you wanted to know how many people were born in Seattle between 1980 and 1989. In that case, you could find this information by searching through every record individually or by using an algorithm to organize all those records into individual years and then count them. In both cases, you would need some program or algorithm with instructions on how the machine should conduct it.
Here is where NLP comes into play: instead of humans writing codes for every situation (which would require them to think about every possible scenario), they can use NLP methods. Machine learning algorithms are capable of learning from their own mistakes.
Natural language processing is essential because it allows machines to interact with humans in a way that feels natural. For example, you can ask Siri questions and receive answers in plain English—rather than dealing with complex programming languages or commands.
Natural language processing is one of the most exciting areas of AI research today. NLP is the ability of computers to understand and process human language. It plays a massive role in the development of AI.
NLP is used extensively in voice assistants like Amazon's Alexa, Apple's Siri, and Google Assistant. These assistants can understand what you say and respond accordingly, which is incredible. Intense work goes into making machine/AI assistants sound natural when they respond.
They have to be able to answer questions about schedules or alerts about upcoming events' reminders; they have to know what kind of information is shareable with whom, and they have to know how to respond when confronted with something inappropriate or off-topic. All this requires extensive training with humans willing and able to provide feedback on how well the assistant understands what was said and what needs improvement before it can go live on the market.
This technology can be used for several things, including:
- Helping people who don't speak English understand the meaning behind words spoken—understanding what people are saying so that you can respond appropriately.
- Understand human speech patterns, allowing us to communicate with machines more naturally.
- Create more intelligent chatbots and virtual assistants that can respond as humans would.
- Helping people find information on the internet if they aren't sure how to phrase their search query.
- Help machines understand the written text better than ever before, which will help them make better decisions related to translation services or robotic surgery procedures (for example).
The two methods of NLP
Syntactic analysis and semantic analysis are two methods of text analysis. The syntactic analysis breaks down a sentence into its parts and determines how those parts are arranged in relation to each other. The syntactic structure can also determine the type of sentence — whether an imperative, declarative, or interrogative statement, for example. Semantic analysis is the process that determines the meaning of a word or phrase by analyzing its relationship to other words and phrases in context. This might mean looking at the relationship between individual words or groups of words.
A good example would be:
"The dog ran away".
Using syntactic analysis, we can see that this phrase has three parts: "the", "dog", and "away". Each word contributes something different to the overall meaning of the sentence—so we can see that each part must be considered when trying to understand what it means. However, with semantic analysis, we would look more closely at each word individually.
Imarticus learning offers a deep-dive post-graduate course that takes you through the basics of NLP and other vital subjects required to learn data science, spread across six months of an integrated course for a successful data scientist career. Book a call with us today or walk into our offline centers to know more about the course and its benefits.