Our team specializes in using reinforcement learning to build dialog systems and software agents for applications in customer service, sales, HR, and marketing. We developed a customer service chatbot to automatically answer questions that came through the City of New York’s 311.
Dialog modeling is used for building dialog systems and finding its applications in various business enterprises like healthcare, education, entertainment, etc.
Answering customer questions about products and services
Facilitating transactions through guidance in the sales process
Responding to customer questions
Website navigation: Guiding customers to relevant portions of complex websites.
Personalized services. For eg: answering questions related to orders, bookings, etc.
We help build and incorporate dialog systems into applications that can benefit your business from a dialog based approach.
What is NLP?
Natural language processing (NLP) is a branch of artificial intelligence that associates with human and computer. It give a computer the ability to understand human language as it is spoken or written. Learning NLP is like learning the language of your own mind!
Natural language processing is a technology that helps computers communicate with humans in their own language and scales other language-related tasks. For example, NLP enables computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important.
Today’s machines can analyze more language-based data than humans consistently in an unbiased way. Considering the floundering amount of unstructured data that’s generated every day, from medical records to social media, automation will be critical to fully analyze text and speech data efficiently.
Basic NLP tasks include tokenization and parsing, lemmatization/stemming, part-of-speech tagging, language detection and identification of semantic relationships. NLP tasks break down language into shorter, elemental pieces, try to understand relationships between the pieces and explore how the pieces work together to create meaning
NLP is used for extracting structured information for analysis, visualization, trending, or alerts. It is also used to extract appropriate info in a format that can be factored into algorithmic trading decisions.
Document classification means assigning a document to one or more classes or categories. Our engineers can help you figure out the ontology of the document classes; work with your team to clean the data and build scalable classification solutions that can be used for classifying variety of documents from emails, user manuals, news, chats, etc.
Dialog modeling is used for building dialog systems, finding its applications in business enterprises, healthcare, education and entertainment among other fields. We can help build and incorporate dialog systems into applications that could benefit from a dialog based approach.
Natural language processing or NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data.
Making data accessible becomes increasingly important as the world's information is going online in a good pace. The challenge of making the information accessible to everyone, across language barriers, has simply outgrown the capacity for human translation. The challenge with machine translation technologies is not in translating words, but in preserving the meaning of sentences, a complex technological issue that is at the heart of NLP.
Information overload is a challenging phenomenon in today’s digital era, and our capacity to understand it is far exceeded by accessible knowledge and information. It is vital in allowing us the ability to recognize and absorb the relevant information from immense pool of data. Thus, NLP technology will become increasingly useful as a valuable marketing asset for summarizing and making analysis simple.
Spam filters is becoming substantially important as the first line of defense against the ever-increasing problem of unwanted email. Almost everyone that uses email extensively has experienced agony over unwanted emails that are still received, or important emails that have been accidentally caught in the filter. The false-positive and false-negative issues of spam filters are at the heart of NLP technology.
Search engines are helping us put the world's information easily accessible, but are still generally quite primitive when it comes to actually answering specific questions posed by humans. NLP helps to recognize natural language questions, extract the meaning, and provide the answer.
Talking about financial markets, crucial decisions are moving away from human control. What’s becoming more popular is algorithmic trading which is a form of financial investing under control of technology. A major task of NLP is taking these plaintext announcements, and extracting the relevant info in a format that can be factored into algorithmic trading decisions.