Applications of NLP

Among the thousands and thousands of species in this world, solely homo sapiens are successful in spoken language. From cave drawings to internet communication, we have come a lengthy way! As we are progressing in the direction of Artificial Intelligence, it only appears logical to impart the bots the ability of language and conversation that is natural to humans. This is where NLP performs its phase as a subset of AI to construct systems that can recognize the language. Throw in Machine Learning(another outstanding AI subset) and voila, we can construct structures that can recognize the language, analyze, and enhance over time besides being programmed explicitly.

1. Chatbots: Almost each and every different internet site currently is being supported by means of a bot that is designed to make our journey better and simpler. Chatbots are the bots designed for a particular use of interplay with human beings or different fellow machines the use of the strategies of AI. Chatbots are  designed maintaining in mind the human interaction. The use of Chatbots goes way returned to 1966 when the first chatterbot named “ELIZA” was once designed at MIT. Eliza could hold the dialogue flowing with the human it interacted with, this led to the improvement of chatbots that may want to have a wonderful impact on human beings struggling from psychological issues.

2. Text Classification: Texts are a form of unstructured information that possesses very prosperous records inside them. Text Classifiers categorize and arrange exceptionally a great deal with any form of textual content that we use currently. Since texts are unstructured, analyzing, sorting, and classifying them can be very challenging and time-consuming and occasionally even tedious work for humans, no longer to point out all the mistakes that human beings are susceptible to make in the process. This is where Text Classification comes in to picture to serve its motive of performing the stated duties with greater scalability and accuracy. Text Classification is more efficient when we talk about ML classifiers which are trained with some important rules and guidelines. With the methodologies of Deep Learning such as CNN and RNN the outcomes solely getting better with the improved textual content information that we generate. It can additionally be made visually attractive to the usage of “Word Clouds”. Text classification can be utilized for a range challenge from electronic mail spam filtering to manufacturer monitoring. A very fundamental key and remarks for commercial enterprise would be how their merchandise is touching their meant buyers and Text Classification offers solutions to enterprise questions with the aid of classifying people’s opinions on the stated brand, price, and aspects.

3. Sentiment Analysis: Feedback is one of the fundamental factors of true communication. Be it a brand-new film or a trendy tech that’s currently launched, the response of the supposed target audience is what makes or breaks them. Hence, inspecting people’s sentiment in the direction of a product is necessary now greater than ever. The Bag of words(BOW) strategy where the authentic order of word is lost, however, the sentence below watch is decreased to the words that clearly make a contribution in figuring out the sentiment is pretty famous for sentiment analysis. This approach makes use of statistical techniques to group the words and the language takes a backseat. The BOW can be thought of as a large dictionary that where every word holds its very own unique cost which contributes to conclude the sentiment.

4. Machine Translation: Achieving multilingualism can frequently be a challenging mission to accomplish, so to make our lifestyles simpler at least in the factor of communication, Machine Translation comes to the rescue. In the early ’50s, IBM introduced a machine translation system that had solely 250 words and translated forty-nine cautiously chosen Russian sentences in the area of chemistry into English. Over the current years with the assets to put in force Neural networks, machine translation has drastically elevated in its high-quality such that translating between languages is as easy as urgent a button on the reachable smartphones or tablets. Google Translate helps greater than one hundred languages and can even translate language pictures from up to 37 languages.

5. Virtual Assistants: Its the part of our day to day life, maybe we didn’t realize it but we are actually dealing with Virtual assistant every day. From placing an alarm to making the grocery list to entertain you while you are feeling bored, virtual assistants play a big phase in our everyday routines. They are engineered to take delivery of the user’s voice instructions and operate the assignment entrusted with them. Virtual assistants are designed to engage with human beings in a very human way, most of their responses would experience like the responses you would acquire from a pal or colleague. In addition to NLP virtual assistants additionally focuses on Natural Language Understanding so as to maintain up with the ever-growing slangs, sentiments, and intent at the back of the user’s input.

Virtual Assistants study from Artificial Neural Networks and can maintain any dialog for a longer period than chatbots. They even serve as basic examples of speech to textual content conversion and textual content to speech conversion. Virtual Assistants can additionally be given greater complex duties such as decision making, and they mature with every interplay and can supply an extra customized journey.

6. Speech Recognition: NLP can be used to recognize speech and convert it into text. This can be used for applications such as voice assistants, dictation software, and speech-to-text transcription.

7. Text Summarization: NLP can be used to summarize large volumes of text into a shorter, more manageable format. This can be useful for applications such as news articles, academic papers, and legal documents.

8. Named Entity Recognition: NLP can be used to identify and classify named entities, such as people, organizations, and locations. This can be used for applications such as search engines, chatbots, and recommendation systems.

9. Question Answering: NLP can be used to automatically answer questions posed in natural language. This can be used for applications such as customer service, chatbots, and search engines.

10. Language Modeling: NLP can be used to build models of natural language that can generate new text. This can be used for applications such as chatbots, virtual assistants, and creative writing.