Smart Data: Natural Language Processing – From Chatbots to Artificial Understanding
About the Webinar
Natural Language Processing (NLP) is rapidly becoming a requirement for application interfaces. Today, one can build a system that allows natural language text or speech input without knowing much more than a few API specs. From chatbots to search engine translation services to applications that scour social media posts looking for business opportunities or terror threats, Natural Language Understanding (NLU) is delivering value today.
On the output side of the interface, natural language generation (NLG) has advanced from the production of restricted-domain question-answering and simulation systems to the delivery of general purpose data- or model-driven narratives that are virtually indistinguishable from human-generated correspondence. From sports to stock reports, you’ve probably read a machine-generated report in the past year without realizing that the “author” was a machine.
Where do we go from here? It’s time to expand our definition of “natural language” to include the logical and physical context cues that we use in everyday communications to express emotions and intent unbounded by syntax and semantic constraints.
In this webinar, we will look at emerging technologies to improve understanding by analyzing effect in gestures and language choices, and that improve engagement by delivering context-appropriate effective output.
About the Webinar
Natural Language Processing (NLP) is rapidly becoming a requirement for application interfaces. Today, one can build a system that allows natural language text or speech input without knowing much more than a few API specs. From chatbots to search engine translation services to applications that scour social media posts looking for business opportunities or terror threats, Natural Language Understanding (NLU) is delivering value today.
On the output side of the interface, natural language generation (NLG) has advanced from the production of restricted-domain question-answering and simulation systems to the delivery of general purpose data- or model-driven narratives that are virtually indistinguishable from human-generated correspondence. From sports to stock reports, you’ve probably read a machine-generated report in the past year without realizing that the “author” was a machine.
Where do we go from here? It’s time to expand our definition of “natural language” to include the logical and physical context cues that we use in everyday communications to express emotions and intent unbounded by syntax and semantic constraints.
In this webinar, we will look at emerging technologies to improve understanding by analyzing effect in gestures and language choices, and that improve engagement by delivering context-appropriate effective output.
