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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

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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New directions in the analysis of massive multiagent systems

Distributed algorithms (that is, algorithms whose pieces run concurrently and independently on many, fallible, interconnected processors, each with a limited amount of information) for problems

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Agent cooperation in large scale environments: from quantity to quality through organized cooperation

In this paper we consider environments that consist of a large set of relatively cheap and simple agents and a large set of objects. The

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Incremental negotiation and coalition formation for resource-bounded agents: preliminary report

We explore a class of task allocation mechanisms that are incremental and can be tuned to the computational resource limitations of agents. Our focus is

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Open-ended planning: extended abstract

Most real-world planning problems arise in incompletely specified situations. One rather computationally expensive way to cope with such incompleteness is to formulate comprehensive contingency plans.

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A commonsense language for reasoning about causation and rational action

Commonsense causal discourse requires a language with which to express varying degrees of causal connectedness. This paper presents a commonsense language for reasoning about action

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Introspective and elaborative processes in rational agents

This paper explores the design of rational agent architectures from the perspective of the dynamics of information change. The procedural elements that guide an agent’s

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Explanatory update theory: applications of counterfactual reasoning to causation

A stratified view of causal reasoning is set forth; one in which the identification of counterfactual dependencies plays an important role in determining what sort

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A survey of research in distributed, continual planning

Complex, real-world domains require rethinking traditional approaches to AI planning. Planning and executing the resulting plans in a dynamic environment implies a continual approach in

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