NLP or Natural Language Processing is basically an approach to find information out of a text to make it understandable to a machine as the same as humans do.
As we all know, the whole idea of machine learning is to provide the human-brain-like capabilities to a machine. This is done to provide the same ability to machines as our human brain, which is capable of understanding text and speech.
Doing so makes these machines able to automatically understand a text or speech in its natural form.
We read so many texts through emails, web pages, apps, etc. Can you imagine if a machine could itself understand this information, how much automation can be done in the field of text manipulation and sentiment analysis?
Natural language processing is a hot topic now but has been studied for the past 5 decades. We come across several applications of NLP in our daily lives.
One of its applications that is used a lot nowadays is Amazon Alexa. Have you ever imagined how it recognizes your voice and follows your instruction?
All of this is made possible with the help of Natural Language Processing. Machines nowadays are being more and more capable of understanding and manipulating text and speech.
Get ready to understand the working and real-world applications of NLP.
Also, read our blog on NLP trends in 2021
First of all, extracting meaning out of a text is truly a challenging job. When we know that we are turning something difficult to possible in machine learning, we use pipelining.
This means we take a number of small steps in order to complete a whole project. These many small steps in NLP pipelining, keeping in mind that we have to find out meaning out of texts, we will do step by step illustration.
When we are given to analyze a document, we know that not every sentence in a paragraph is in sync with each other totally, or in general, every sentence has an individual meaning, so consider the below text :
“Calcutta now Kolkata was the capital of India during the British Raj, until December 1911. Calcutta had become the center of the nationalist movements since the late nineteenth century, which led to the Partition of Bengal by then Viceroy of British India, Lord Curzon. This created massive political and religious upsurge including political assassinations of British officials in Calcutta.”
Now see what this paragraph would look after sentence tokenization:
“Calcutta now Kolkata was the capital of India during the British Raj, until December 1911”
“ Calcutta had become the center of the nationalist movements since the late nineteenth century, which led to the Partition of Bengal by then Viceroy of British India, Lord Curzon. “
“ This created massive political and religious upsurge including political assassinations of British officials in Calcutta.”
Every sentence in the above paragraph has been tokenized. You may be able to discern the change or effect on the sentences after reading the paragraph before and after tokenization.
As we tokenized sentences in our first step of pipelining, we tokenize each word in the next step of pipelining. Let's take an above-tokenized sentence and apply the process of word tokenization.
“Calcutta now Kolkata was the capital of India during the British Raj, until December 1911”
After, word tokenization it will look somewhat like this:
“Calcutta”, “now”, “ Kolkata”, “ was”, “ the”, “ capital”, “of”, “ India”, “ during”, “ the”, “British”,“ Raj”, “ until”, “ December”, “ 1911”
In word tokenization, wherever there is a space in between words, we split them. That's how simple it is.
Stemming refers to cutting down the prefixes or suffixes of words to extract some meaning out of them. However, this technique does not ensure that the word will have some meaning.
For example, studying, here the suffix ‘ing’ will be cut, leaving the remainder ‘study’, which is correct. But in the case of ‘studied’, it will cut into ‘ed’ and ‘studi’, which is, of course, incorrect.
On the other hand, lemmatization is a process where the lemmatized word will definitely have some meaning. “Bag of words” is a tool used for stemming and lemmatization of words.
Other techniques used in pipelining are the identification of stopwords, which can be easily done with the help of the python library NLTK.
Named Entity Recognition(NER) technique, where we tag out the entities which can be famous people, places, products etc. to sort more meaning and part of speech recognition for identification of speech.
Above are some techniques that help the machine to understand the syntax and semantics of natural language.
Analyzing text and giving them remarks that can be either positive or negative, in order to analyze the context of a text is known as sentiment analysis.
For example, if we have to analyze the reviews given by the public to a movie through comments, then a given set of sentences or words will be given remarks, such as positive or negative. After that, all positive and negative remarks will be counted to find out the average ratings for the movie.
Nowadays, to help customers with real-time questions and answers, almost every web product or application is using chatbots as one of the topmost preferences.
The reason behind the increased use of chatbots is that it is an economical method to provide a personalized assistant experience to users. Machine learning chatbots are witnessing a surge in use.
Bot conversations in many organizations are recorded as ratings that reflect the feelings of users, to get an idea of the behavioral pattern of the market.
We encounter many chatbots on a daily basis that are using NLP to handle its users.
Companies like Zomato, Uber, Banks have chatbots integrated with their customer service channels, handling off conversations, back and forth, while people can take on more complex and bigger conversations.
There are many great examples of NLP-based chatbots like X.ai, Xiaoice, Mitsuku etc.
It is a process of translating one natural language to a target language. For example, you must have used google translator to translate sentences in English to Hindi or any other language. This shows how useful this technique is.
Machine translation is sometimes not efficient enough because translating one language to another language, finding the perfect counterparts, and preserving the meaning of the phrase requires advanced statistical and NLP techniques.
Machine translation is one of the oldest subfields of artificial intelligence research and there are 4 types of machine translation.
Speech recognition can be seen in many fields, whether it be a home automation device such as Google Nest, Amazon echo, or assistants like Amazon Alexa, Google Assistant, and Apple Siri is also a good example of speech recognition.
Natural Language Processing
Social Media platforms have witnessed a huge increase in their use in the recent past. Almost Everyone these days is using at least one social media platform.
This increased use of social media leads to a generation of lots of data. This data is then analyzed. NLP is used by companies to understand consumer behavior like their preferences, how much they like a product or a service.
Companies also use social media monitoring to look after the issues faced by their customers. Not only private firms, government organizations also leverage social media monitoring to identify potential threats to national security.
You may have come across Grammarly and you may be also familiar with its working.
If not, Grammarly is used for the correction of grammatical errors in a document. It highlights the grammatical mistakes in a document and suggests the right word. But how does it work?
It uses Natural Language Processing to correct grammar, suggest synonyms, identify spelling mistakes, and deliver content with more clarity and engagement.
We all use Gmail, don’t we? Yes, we do and also enjoy the filtering it offers. We receive tons of emails and Gmail classifies that into categories like primary, social, promotions, and spam. But how does that happen?
Email applications like Gmail make use of text classification that is an NLP technique, to filter our emails. As the name suggests, text classification is the process of classification of text into predefined categories.
Till now we have seen NLP in power but its working has never been as simple as it is shown.
In fact, it is a very complex technique to understand under deep learning, as there are so many syntaxes and semantics in natural language that it is difficult for a human to master and we are here trying to make this complex thing possible through machines.
Over the past, NLP got powered by machine learning algorithms to produce some very optimum results.
Natural Language Processing is a technique that is going through advancement, every single day and the day it will come to its full potential, it will create miracles in the automation sector.
Natural language processing in deep learning is a very demanding and promising field of the 21st century. We see towards more advancement as we know it has not grown to its full potential, but with further association with new machine learning algorithms, we can see some more of its application in daily life.
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