Are you trying to explore something on how to learn NLP or what are the best resources to learn in NLP? If you're, then this article will be a good start for you. Read thoroughly and you will get everything.
Natural Language simply means the language that we use on regular basis for communication. It can be any language like English, Hindi, Spanish etc. But when it comes to computers, they can hardly understand our natural language. To make them understand our natural language, we make use of the technology known as "Natural Language Processing". Though we are using technologies to make the machines understand our natural language but its still not an easy job. The study of "NLP" has been around for more than 50 years and it grew more with the rise of computers. NLP, in wide sense, can be defined as computer/automatic manipulation of natural language by software and it can be anything like speech or text.
In other words, it can be defined as the capability of a computer program to understand human language and development of such applications is quite challenging as natural language is highly ambiguous and is always changing and evolving due to its linguistic structure depending upon many variables, dialects, regional context etc . We have poor rules that govern a language though we are very good at understanding, perceiving, expressing and interpreting a language.
NLP relies on machine learning as well as deep learning depending on data to acquire meaning from human languages. It is also termed as a computational technique for scrutinizing and synthesizing natural language and speech. Computational linguistics can be understood as a study or creation of tools/computer systems for tasks such as machine translations, generating natural language, speech synthesis, information extraction, speech recognition, text mining etc.
You will take an overview of "Natural Language Processing" and how to use "machine learning" methods in this NLP tutorial for beginners and for the developers as well. So today in this article, you'll be given a thorough overview of Natural Language Processing, and help you to understand its work process, why NLP is important and what are the applications specifically created via Natural Language Processing.
So what are you waiting for? Let's take a dive & know something about Natural Language Processing
AN OVERVIEW OF NLP
Natural Language Processing (NLP) is the driving force behind any software tool that is aimed to interpret, understand, and determine the actual meaning from human language in an intelligent and in a more valuable way. 'Natural Language Processing' is seen as Computational Linguistic study as well. Here the ultimate purpose of NLP is to read, interpret, understand, and make sense of the human languages in a manner that is more valuable than anything. In other words, learning NLP is like learning the language of your own mind!
NLP is a very challenging as it is the human language that makes it quite difficult. Approaches used for NLP earlier were mostly rule-based and machine learning algorithms were applied mainly. It was limited to looking for specific words/phrases in given text and give specific responses when those phrases or words appeared. The main failure of this approach was that machine can't answer the words or phrase which didn't appeared in the data being trained with. This eventually led to the development of deep learning algorithms for NLP. They are more flexible and can easily learn almost like how a child is made to learn any human language. Deep learning examines and uses patterns in data to fulfill its motive of learning human language. For this, massive amount of labeled data is required for training the model and identifying correlations but handling and structuring this massive data is itself a current challenging task.
Rules controlling information flow using natural language is not easy for computers to understand. NLP requires algorithms that can identify and extract natural language rules from a small/huge unstructured data and converting it to a form that computers can understand.
Note: You can take an overview of Natural Language Processing in the given short video below. We are sure that you will grasp things better.
What can be done with NLP?
1. Text Classification: IT classifies text documents or sentences into one or more defined categories. Spam detection and sentiment analysis are applications of text classification.
2. Natural Language Understanding: It is a subset of NLP which uses algorithms that goes beyond understanding and interpreting words and their meanings to reduce human speech into a structured ontology. It is mainly used for creating bots or cognitive assistants that can interact with public without supervision. Companies working on NLU include Medium's Lola, Amazon's with Alexis and Lex, Apple's Siri, Google's Assistant and Microsoft's Cortana.
3. Machine Translation: Translating text or speech from one language to another.
4. Natural Language Generation: This basically involves databases for deriving semantic intentions and later on converting them into human understandable language.
5. Sentiment Analysis: Identifying the mood or tone of a statement whether it is negative, positive or neutral. It is mostly used on social media comments by organizations to review their products feedback from customers.
6. Topic Modelling: This technique is used for discovering topics based on their contents in any textual document. It assumes that every textual document consists of many topics and each topic consists of words or statements. So, it spots the topic in a document which can therefore unlock the meaning of our document.
7. Statistics of Document: We can evaluate how "good" a topic in document is by calculating some stats. Some of these are perplexity, coverage, topic variety, topic coherence, confidence score etc.
There are many more things that can be done with NLP. More can be found here
Let's take a look at some of the major applications that mainly designed with the help of Natural Language Processing (NLP).
APPLICATIONS OF NATURAL LANGUAGE PROCESSING IN AI
NLP is also known as computational linguists as well. With the help of NLP, there are many of the applications designed that can be easily run on iOS and android. On your iPhone or iOS device ‘Hey Siri’ and on your android device ‘Hey Google or Hey Alexa’ are the products that perform impeccably with the ‘Natural Language Processing’.
Image Source: Giphy
Image Source: Giphy
Image Source: Giphy
Moreover, some of the most prominent applications of ‘ Natural Language Processing’ for businesses. Read below you will get clear about such applications.
Livox is an alternative communication app for tablets. The only one which can be used for communication and also in the process of education of persons with disabilities!
Google Translate is a free multilingual machine translation service developed by Google, to translate text. Google Translate supports over 100 languages at discrete levels and serves around 500 million people daily.
A chatbot is an artificial intelligence (AI) software that can simulate a conversation (or a chat) with a user in natural language through messaging applications, websites, mobile apps or through the telephone. Below is the example we have shown to you that mainly performs with the chats. It is one of the shiniest chatbot on the web.
Why NLP is important?
Nowadays, the evolution of Natural Language Processing applications is quite challenging, because computers traditionally work explicitly, unambiguous and extremely structured languages such as python and other programming languages, however, the natural language is often ambiguous and the linguistic structure can depend on many intricate variables, including dialect, slangs, and the social context.
As a human, you may speak and write in English or Spanish. Although a computer’s primary language associated with machine code or machine language — which is most difficult for most the individuals. At your device’s lowest levels, the means of communication befalls not only with the words but through zillions of zeros and ones that generate legitimate actions.
On the other hand, if we talk about today's machines they can easily interpret more language-based data than humans, without any exhaustion and in a logical, impartial 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 is important because it improves and resolve ambiguity in language and adds useful numeric structure to the data for many downstream applications, such as speech recognition or text analytics.
Benefits of NLP
NLP hosts benefits such as:
- Improved accuracy and efficiency of documentation.
- The ability to automatically make a readable summary text.
- Useful for personal assistants such as Alexa.
- Allows an organization to use chatbots for customer support.
- Easier to perform sentiment analysis.
In this blog, we have explained the basics of Natural Language Processing and what are major applications that mainly created with NLP. This section provides more resources on the topic if you are looking to go deeper you can devour from given books links.
For Further Reading
- Mathematical Linguistics, 2010.
- Neural Network Methods in Natural Language Processing, 2017.
- Computational Linguistics: An Introduction, 1986.
- The Oxford Handbook of Computational Linguistics, 2005.
- Foundations of Statistical Natural Language Processing, 1999.
- Natural Language Processing with Python, 2009.
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