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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

Intuitionistic Modal Logic: A 15-year retrospective

  The series of workshops on Intuitionistic Modal Logic and Applications (IMLA) owes its existence to the hope that philosophers, mathematical logicians and computer scientists

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Seeing is Correcting: curating lexical resources using social interfaces

This note describes OpenWordnet-PT, an automatically created, manually curated wordnet for Portuguese and introduces the newly developed web interface we are using to speed up

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A linked open data architecture for the historical archives of the Getulio Vargas Foundation

This paper presents an architecture for historical archives maintenance based on Open Linked Data technologies and open source distributed development model and tools. The proposed

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As Wordnets do Português

Not many years ago it was usual to comment on the lack of an open lexical-semantic knowledge base, following the lines of Princeton WordNet, but

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Explaining Watson: Polymath Style

Our paper is actually two contributions in one. First, we argue that IBM’s Jeopardy! playing machine needs a formal semantics. We present several arguments as

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Exploratory Information Extraction from a Historical Dictionary

We describe a preliminary project of extracting information from an extant dictionary of historical biographies, the “Dicionario Historico-Biografico Brasileiro” (the Brazilian Historical and Biographical Dictionary,

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Linear Logic Model of State Revisited

In an unpublished note, Reddy introduced an extended intuitionistic linear calculus, called LLMS (for Linear Logic Model of State), to model state manipulation via the

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Perspective-taking behavior as the probabilistic weighing of multiple domains

Our starting point is the apparently-contradictory results in the psycholinguistic literature regarding whether, when interpreting a definite referring expressions, listeners process relative to the common

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A TV Program Discovery Dialog System Using Recommendations

We present an end-to-end conversational system for TV program discovery that uniquely combines advanced technologies for NLU, Dialog Management, Knowledge Graph Inference and Personalized Recommendations.

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Belief Tracking with Stacked Relational Trees

  We describe a new model for Dialog State Tracking called a Stacked Relational Tree, which naturally models complex relationships between entities across user utterances.

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