Yogesh-R | March 16th, 2022
NATURAL LANGUAGE PROCESSING: Booming field of Artificial Intelligence

NLP & Human-machine Interaction

 

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Humans are surrounded by text in the form of signs, mails, conversations, tweets and a lot more.

Language is a principal method of communication among humans, consisting of words framed in a structured and conventional manner. Anything which is conveyed by speech, writing, or gesture contains a huge amount of information. The selection of words, their arrangement, and intonation help to find out the intent of the message/statement delivered by any means. Being a human, we can understand and predict the behavior and actual sense of the message even if it is a mix of words from different languages.

But the problem arises when the information has to be interpreted by a machine. A person may generate thousands of words, with different tones and each sentence with its relative complexity. Analyzing and interpreting different combinations of words makes it unmanageable for the machine to figure out the exact meaning.

Earlier the text or the speech was interpreted on the basis of keywords only but the need to figure out the meaning behind those words cognitively to get its actual sense like sarcasm, irony, etc. leads to the need to explore Artificial Intelligence in the field of Natural Language Processing (NLP).

What is NLP?

Do you know how Alexa understands your language & provides you the solution in the blink of an eye?

The amazing feature of NLP enables the machine/Alexa to read and hear messages, interpret it and implement sentiment analysis on it. 

NLP can be defined as a field of artificial intelligence that gifts the machines the ability to read, understand and derive appropriate meaning from human languages. 

Some examples which we use in day to day life are:

  • Spell check
  • Autocomplete
  • Voice text messaging
  • Spam filters
  • Related keywords on search engines
  • Siri, Alexa, or Google Assistant.

It is a branch that specifically helps computers/machines to understand, interpret and manipulate the language used by humans to fulfill the objective of filling the gap between machine understanding and human communication. NLP is such an incredible feat that has huge potential to impact so much in our modern existence. 

Significance of NLP

Human language is highly ambiguous, a word can have different meanings in different languages which are again very challenging to figure out. After getting all the meanings, it is equally tedious to find out the right one in the context of that statement.

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NLP understands the structure and meaning of human language by examining different patterns, semantics, and morphology which then gets translated into rule-based ML algorithms which perform the desired tasks.

NLP has a wide range of uses. It helps developers to develop software that can perform tasks including automatic summarization, translation, relationship extraction, speech recognition, sentiment analysis, parts-of-speech tagging, topic extraction, text-mining, automated question answering, and more.

Let’s take a better look at the applications of NLP.

  1. Speech Recognition: Siri, Alexa & Google home are taking inputs (in the form of speech), interpret them, understand them and then deliver the desired result as output.

If you want to listen to a song, you simply instruct Alexa to play it. Alexa processes the request, understands it using NLP, searches for the same keywords, and plays the song.

  1. Sentimental Analysis:  The messages, status, tweets, etc on the social platforms are taken and analyzed to find out the feelings, expression, and conclusion towards a particular event. For example The review of a new movie, web series, or any new product. Facebook uses NLP to tail trending news topics and popular hashtags.

This application can also be used during the Elections, through NLP it is possible to keep a track of the statements, speeches delivered by the candidate/party to analyze their aim and find out how focussed they are, to accomplish the goal which in turn contributes in decision making.

  1. Machine Translations: This feature is extremely popular as it overcomes the barriers to communicate with people around the world. It translates the speech, statements written in one language to another language. 

4. Chatbots: The usual chats are conducted between human beings but NLP helps to understand what a human wants, and guides them to get the desired outcome with as little work for the end user as possible. Like a virtual assistant for your customer experience touchpoints. 

If you have ever interacted with a website chatbox while online shopping or any other activity, you actually were interacting with a chatbot instead of a human. These chatbots are the algorithms that use NLP to understand your question and respond after searching for the best-matched answer in their database.

5. Email Filters: This feature saves a lot of time. Spam filters work on the basis of the keywords/phrases used in the subject, it specifies it as a spam message.

You must have experienced that now, we get the emails classified as primary, social, or promotions based on their contents. Using the key extraction task of NLP, the subject lines are scanned and moved to the concerned folder.

6. Search results: NLP is used extensively by search engines. It is used to discover relevant results based on the keywords searched frequently or the users’ intent which helps to determine what they are looking for without being a search term wizard. 

As soon as you type words in the search box, google predicts the popular searches relevant to the query.

Natural language Processing is not limited to social platforms, it is booming in this digital world. There is a vast range of business use cases and experiments to make complex applications simple and possible by continuous enhancements in NLP. Other practical uses of NLP include monitoring for malicious digital attacks, such as phishing, or detecting when somebody is lying. 

Despite the ambiguity in the languages and challenges of the future, NLP is developing exponentially and practitioners are working passionately on NLP in areas like healthcare, media, finance, and human resources to turn up trumps.