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, and entertainment.
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 such as answering questions related to orders and bookings.
We help build dialog systems and incorporate them into applications that can benefit your business from a dialogbased approach.
What is NLP?
Natural language processing (NLP) is a branch of artificial intelligence that associates with human and computer. It gives 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 and hear speech and to interpret what it hears measure sentiment, and determine which parts are important.
Today’s machines can consistently analyze more language-based data than humans in an unbiased way. Considering the astounding amount of unstructured data that’s generated every day, from medical records to social media, automation will be critical to fully analyzing 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 and work with your team to clean the data and build scalable classification solutions that can be used for a variety of documents such as emails, user manuals, news, and chats.
Dialog modeling is used for building dialog systems and finding their 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 at 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. NLP is vital in allowing us the ability to recognize and absorb the relevant information from an immense pool of data. NLP technology will become increasingly useful as a valuable marketing asset for summarizing and making analyses simple.
Spam filters are 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 make 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.
In 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 the control of technology. A major task of NLP is taking these plaintext announcements and extracting the relevant information in a format that can be factored into algorithmic trading decisions.