Indic NLU a state-of-art framework understand's local Indian language and includes functionalities such as part of speech tagging, lemmatization, phrase extraction, text categorization, entity extraction, topic extraction and parsing. Proceed to the demo and if you are interested in getting more information about our dictionaries and how you can use them for different applications like parsing, topic extraction or text categorization connect with us.
MED NLU Framework taps into the power of unstructured data. NLP is used within and atop existing systems and technology applications to support better data-driven decision making in five key operational areas:
1.Extracting and segregating relevant texts
2.Creating a structured database from unstructured data
3.Chatbot - A recommendation system
4.Personal Assistance - (Clinicians) an intelligent system will have the capability of holding and considering experience from different doctors to treat a patient.
5.Clinical documentation improvement.
MEDNLU be in the background of more visible technologies that make work easier for physicians, coders, case managers, and many others who rely on clinical data within an organization.
Arnekt kick-started developing the sentiment analysis framework, by crawling about few lakhs of news data in real-time and extracted sentiment information from them. Predictive models were built using these datum’s, With data in few millions, it was necessary for us to go along with Deep Learning. Most recent research was on applying Convolutional Neural Network (CNN) to applications that does not involve Computer Vision (CV). It is when CNN had its optimum growth in the field of NLP. Researchers have started experimenting with CNN (then an ideal tool for CV applications) on text data to bring out state-of-art models using the same.
We are able to extend our experimentation to a product level deployment, thereby ending up with prominent results in Sentiment analysis of news datum’s, the unsupervised methodologies helped in ending up with a well-established tagged corpora for assembling a reliable product.
Text summarization distills the most important information from a source (or sources) to produce an abridged version for the user to use it, Automatic text summarization framework produces a concise and fluent summary while preserving key information content and overall meaning.
Artificially intelligent Bholu can interact with common people in their local language and helps them with information on trains, schedule, ticket availability and also help book tickets! This NLP & ML based framework, can be used as a base to create a scalable & robust information service system for the Railways.
An application based on Natural Language processing which would be an interactive system, Perform book search, Check for books availability, Check for recommendations, Check for other books which are similar, Reserve a book
1. Interesting technical innovation ensures that the user uses/refers the manual
2. Easily available and Can be downloaded from the Server and be made available on the smart phone at any given time.
3. Ease of Use Anyone can operate the product by searching relevant section through natural speech
4. Go Green!
The sole aim is making positivity a new habit, when any person acquires new habits, the brains acquire "mirror neurons" and develop a positive perspective that can spread to other people like a virus.
Let us reprogram our brains to experiencing the GoodEarth!
Arnekt chatbot helps the address customer queries effeciently, deep learning models enahnce the user experience. The application is easy to use and efficient and enhances customer experience.
Our voice bots have the potential to engage in dialogue based systems from Voice industry and enhance the productivity of the Back offices and customer engagements.
NLP frameworks powers the customers to organize a variety of unstructured data in various formats viz. purchase orders, invoices, contractual documents into structured data and push it into ERP systems etc.