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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

Coherence relation assignment

Three empirical studies of coherence in large corpora of commentary text are sketched, showing that cue phrases are infrequent, and the substantive coherence relations must

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A Dialectica-like model of linear logic

The aim of this work is to define the categories GC, describe their categorical structure and show they are a model of Linear Logic. The

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Intelligent information management in a distributed environment

The problem of intelligent information management in a distributed environment consisting of a collection of interacting problem solvers is discussed. Such environments require applications to

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Partial descriptions and systemic grammar

This paper examines the properties of feature-based partial descriptions built on top of Halliday’s systemic networks. We show that the crucial operation of consistency checking

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A method for disjunctive constraint satisfaction

A distinctive property of many current grammatical formalisms is their use of feature equality constraints to express a wide variety of grammatical dependencies. Lexical-Functional Grammar,

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The first aim of this note is to describe an algebraic structure, more primitive than lattices and quantales, which corresponds to the intuitionistic flavor of

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Description of the Interpretext system as used for MUC-3

Intelligent Text Processing is a small start-up company participating in the MUC-3 exercise for the first time this year. Our system, Interpretext, is based on

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The autonomy of shallow lexical knowledge

The question of what is “purely linguistic” is considered in relation to the problem of modularity. A model is proposed in which parsing has access

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Systemic classification and its efficiency

This paper examines the problem of classifying linguistic objects on the basis of information encoded in the system network formalism developed by Halliday. It is

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Interpretation of textual queries using a cognitive model

The volume of machine-readable text is growing exponentially. In news media, the government, medicine, law, and other fields, machine-readable texts or abstracts have been stockpiled

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