Natural Language Processing is meant to be a method to translate between the computer and the human language. The overall goal of natural language processing is to allow computers to make sense of and act on human language. So, in simple words to say, Natural Language Processing concerns with the interaction between computers and human natural languages, in particular how to program computers to process and analyze large amounts of natural language data.
Importance of Natural Language Processing:
Natural language processing helps computers communicate with humans in their own language and scales other language-related tasks. For example, NLP makes it possible for computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important.
Today’s machines can analyze more language-based data than humans, without fatigue and in a consistent, unbiased way. Considering the staggering amount of unstructured data that’s generated every day, from medical records to social media, automation will be critical to fully analyze text and speech data efficiently.
Natural Language Processing techniques are very useful for many businesses to analyze the customers’ sentiment. It helps to identify the sentiment among several online posts and comments. The business firms make use of natural language processing techniques to know about the customer’s opinion about their product and services from the online reviews.
Working of Natural Language Processing:
Natural Language Processing consists of two areas:
- Natural Language Understanding (NLU) has to do with applying machine learning toward breaking language down into concepts with coherent relationships.
- Natural Language Generation (NLG) is about building up natural linguistic phrases that accurately represent a series of starting concepts.
The two processes are the natural inverse of one another, though both are necessary for true Natural Language Processing success.
Development of both abilities requires incredible volumes of data, collecting and curating data is the most difficult and time-consuming part of machine learning, by far. But every phoneme collected and curated brings the NLP space just a little bit closer to completion — to the day when users can simply speak to their computers and receive a completely natural response in return.
With the ongoing growth of the World Wide Web and social media, there is a drastic increase in online unstructured data. As the amount of data increases, the mechanisms to process these unstructured data and to extract meaningful information from it becomes more challenging.
It will be very difficult to find a specific piece of information from a large knowledge base of unstructured data. These challenges and difficulties can be overcome with the advanced NLP techniques.
No comments:
Post a Comment