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Natural language & AI

Natural Language Processing and Artificial Intelligence technologies enable users to communicate with the digital technology they use everyday, whether for work or recreation.

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Natural language & AI



Powering next gen interactions
Nuance has one of the largest Natural Language Processing and Artificial Intelligence groups in the world with over 140 researchers, most with advanced degrees. This group is distributed among labs around the world extending from Sunnyvale, CA to Montreal, Canada to several locations in Europe. The goal of the research conducted at these labs is to develop the next generation of intelligent, conversational agents powered by Nuance speech infrastructure. The staff has established research reputations in linguistics, dialog systems, computational linguistics, AI, question answering, natural language understanding, and machine learning, as well as systems building, testing and evaluation. These activities are organized into seven subgroups:

  • Linguistic Technologies
  • AI and Reasoning
  • Conversational Systems
  • Question Answering
  • Applied NLP
  • Application Research
  • Clinical Language Understanding in Healthcare

Linguistic Technologies
The Linguistic technologies group focuses on pragmatics, discourse and dialog processing; anaphora resolution; utterance generation; syntactic parsing; data driven methods; entity recognition; morphology; multilingual NLP; and semantic interpretation.

AI and Reasoning
The AI and Reasoning Group performs research in the areas of knowledge representation and reasoning (large scale knowledge bases, logic-based representation tools, knowledge and data integration methods), collaborative dialog systems, and probabilistic reasoning for intent recognition in dialog and ambiguity management in natural language systems.

Conversational Systems
The charter of the Conversational Systems team is to develop advanced conversational systems using components from the Linguistic Technologies and AI and Reasoning teams, other Nuance NLP teams, as well as the larger academic NLP community. Our ultimate goal is to create a mixed-initiative conversational experience with multi-turn dialogs powered by complex reasoning, using technology that is as independent as possible of any one domain.

Question answering
The ability to answer questions is at the heart of any successful conversational assistant. People often need more information before they can make the choices necessary to accomplish a task, and asking questions is how they get that information. Question answering draws on many NL/AI technologies, including parsing, semantics, knowledge representation, inference, problem solving, and dialog. One of the unique aspects of question answering as a task is that the necessary information is often expressed in natural language as well as formal language, requiring a combination of natural language inference and traditional inference techniques.

Applied NLP
The focus of the Applied NLP group is to create technologies that make it easy for application developers to quickly and intuitively build and tune natural language understanding or conversational systems. The group is also involved in the transfer and support of those technologies in Nuance products as well as in the support of many languages.

Application Research
The NLU Application Research team focuses on building scalable and customizable NLU and dialogue solutions, and developing tools and processes that make it easy to build such solutions.

Clinical Language Understanding in Healthcare
The Clinical Language Understanding group works on developing and applying natural language processing algorithms to extract and interpret medical concepts and their relations from medical records which are a mixture of narrative, free-text as well as structured data. This research goes beyond Named Entity Recognition, extracting concepts which are described rather than named and encoding them to their canonical forms such as concepts in SNOMED CT or the Nuance proprietary Ontology LinKBase. Relation extraction is leveraged to keep related concepts together to create medical facts as well as to identify relationships between different facts, such as procedures meant to treat disorders vs procedures performed to diagnose disorders. The group also conducts research in structure detection in medical reports, detection of uncertainty, negation, temporal information, confidence estimation and rule based systems to interpret extracted information for various healthcare applications.

Explore recent publications by Nuance Natural Language and AI researchers.



Selected articles

Distributed sensor networks: a multi-agent perspective

Distributed Sensor Networks is the first book of its kind to examine solutions to this problem using ideas taken from the field of multi-agent systems. The

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Teambotica: a robotic framework for integrated teaming, tasking, networking, and control

TEAMBOTICA is a research environment for the exploration of theories, designs and implementations of team-based robotics. In developing TEAMBOTICA, we found that many of the simplifying

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Cooperative large-scale mobile agent systems

We consider environments that consist of a large set of relatively cheap and simple agents and a large set of objects. The agents should track

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Centibots: large-scale robot teams

As part of the DARPA Software for Distributed Robotics Program, SRI International, Stanford University, the University of Washington, and ActivMedia Robotics are designing and implementing

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Hierarchical information combination process for large-scale task and team environments

 In this paper, we describe the Distributed Dispatcher Manager (DDM), a system for monitoring large collections of dynamically changing tasks. We assume that tasks are

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Interpreting information requests in context: a collaborative web interface for distance learning

We describe the use of theories of agent collaboration and human dialogue processing in providing a principled basis for the design of web interfaces to

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Hierarchical organizations for real-time large-scale task and team environments

In this paper, we describe the Distributed Dispatcher Manager (DDM), a system for monitoring large collections of dynamically changing tasks. We assume that tasks are

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Structured negotiation

Structured negotiation is proposed as a new method through which collaborating agents can seek consensus on the apportionment of tasks and resources. The approach draws

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Multilevel adaptation in teams of unmanned air and ground vehicles

We describe ongoing work to develop algorithms and software for the reprogrammable, coordinated command and control of teams of autonomous vehicles (AVs). This new software

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Dynamic negotiation

We present an anytime algorithm for adapting a negotiation to a dynamically changing environment in which either new tasks can appear or the availability of

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