Natural language process is one of the Latest topics of AI.
NLP refers to the AI method of communicating with an Intelligent system using natural language. By utilizing NLP & its component one can organize the massive chunks of textual data and perform numerous automated tasks. NLP can solve a wide range of problems such as automatic summarization, machine translation, named entity recognition, speech & topic segmentation.
NLP is basically how computers can process, analyse, and understand human languages.
Machines use NLP to communicate.
It is a subtask of information extraction & the process of identifying words that are named entities in a given text. Any work which represents the person, brand, place is the named entity. There are many named entities like medical codes, addresses, dates, quantities, percentages, etc.
POS tag is a process of Converting sentences to Forms basically like words. Tuples & Tagging signifies whether the words are nouns, adjectives, verbs, etc.
NLP text summarization means taking all the information & converting it into a condensed version of the information that covers the important points.
In NLP, the sentiment analysis process is a technique that is used to analyze whether the data is positive, negative, or normal. Basically used for customer feedback & understanding Customer needs.
In NLP, Text Classification means analyzing the text & based upon its tags, Context which is assigned to the Group or community. Topic detection & Language detection is also a part of NLP classification.
Language models help & distinguish between words & phrases. Examples of NLP Language Modeling are speech recognition, machine translation, part-of-speech tagging, parsing, handwriting recognition, Optical Character Recognition, information retrieval, etc.
Customer feedback & interaction is very important in every organization but Manual interactions are not easy every time... to resolve the concern “Chatbots” introduced & are very successfully used by companies. For a smooth customer experience Chatbot is the best solution.
These are autocorrected & autocomplete. Many of the companies have started using this amazing feature of the NLP technique. Google search is the one place where you get to see Autocorrect and Autocomplete features.
Google translator is the best example of language translation. Automatically translating the text into another language without changing its meaning. NLP Language modeling is the technique used here.
Various companies are using Social media monitoring to understand their products, policies, etc. NLP techniques are used by Companies in Social media to know the likes & dislikes of the product.
Surveys are very important & crucial tasks in various organizations. Nowadays to analyze the customer’s feedback & understanding the flaws & improve the product. The sentiment analysis NLP technique helps a lot of companies to understand the surveys very accurately.
It means when someone searches for any product or service. Based on his online activities he will get ads for those similar products. Targeted advertising Mainly works by Keyword Matching.
Here everyone uses the NLP technique of information extraction or Named entity recognition to fetch Name, skills, Experience to check & analyze the Resumes.
Alexa, Google Assistant, Siri are the example of Voice assistants. Most of the devices are voice assistants now. Speech recognition is used to create such software & applications.
Grammar checkers is one of the widely used applications of NLP. As it simplifies life by providing amazing features & improving the Content health in terms of spells, sentences, proofreading, etc. It helps any ordinary text into a deep meaningful sentence.
Emails are filtered with text classification, Which is an NLP technique. Whenever we get an email it automatically gets filtered as per the category like primary, Social & promotional. And, Spam is filtered in a separate section. This is how Email Filtering works.
E2E Networks helps in Accelerate NLP workloads like machine translation named entity recognition speech & topic segmentation, Spell-checkers, online search, translators, voice assistants.
is suitable for a wide range of uses
Train complex models at high speed to improve predictions and decisions of your algorithms. Use any framework or library: TensorFlow, PyTorch, Caffe, MXNet, Auto-Keras, and many more.
Accelerate Convolutional Neural Networks based deep-learning workloads like video analysis, facial recognition, medical imaging and others
Analyze and calculate large and complex financial data; performtons of transactions in real-time. Do accurate financial forecasting, faster
Design and implement data-parallel algorithms that scale to hundreds of tightly coupled processing units: molecular modelling, fluid dynamics and others
Deal with large-size data sets and continuously growing data, splitting it up between processors to crunch through voluminous data sets at a quicker rate
We at CamCom are using E2E GPU servers for a while now and the price-performance is the best in the Indian market. We also have enjoyed a fast turnaround from the support and sales team always. I highly recommend the E2E GPU servers for machine learning, deep learning and Image processing purpose