Featured NLP Projects

Our faculty are engaged in research projects ranging from language documentation and morphological analysis to semantic analysis to Biomedical Informatics. We are also currently working on an autonomous conversational agent in a junior high through college classroom setting. Featured below are some of theÌýprojects we are most proud of, both past and present.

Ìý ÌýOngoing

JanÌý28th

DARPA AIDAÌýProgram

Autonomous Interperation ofÌýDisparateÌýAlternatives

Project leads

Martha Palmer

Martha Palmer

Susan Brown

Susan Brown

Jim Martin

Jim MartinÌý Ìý Ìý

Chris Heckman

Chris Heckman

Our goal is toÌýautomatically analyze the content of written documents and extractÌýkey pieces of information about the events they describe, including whereÌýdifferent news sources contradict each other.

Problem

We can’t possibly keep track of everything that is happening day to day - in the news, in medicine, in financial markets, on social media, etc.

Solution

Natural Language Processing can automatically extract key events, along with who is participating in them and the order in which they happen,Ìýto help make our job of keeping on top of things much more tractable.

Techniques Used

  • Deep LearningÌý
  • Graph EmbeddingsÌý
  • Coreference ResolutionÌý
  • Type MatchingÌý
  • EntityÌý& Event AnnotationÌý&ÌýRecognition Ìý
  • Ontology ConstructionÌý&ÌýMapping

Ìý

Ìý

ÌýÌý Ìý

Ìý ÌýOngoing

Jan 28th

THYME

Temporal History of Your Medical EventsÌý ÌýÌý

Project leads

Martha Palmer

MarthaÌýPalmer

Jim Martin
Ìý Ìý Ìý
JimÌýMartinÌý Ìý Ìý ÌýÌý

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Kristin Wright-Bettener

KristinÌýWright-Bettener

Our goal is automatically extracting the timeline of a disease and its treatment from patient records. This benefits individual patients and their doctors by providing quick, accurate summaries of a patient’s history covering several years. Moreover, aggregating together timelines for large numbers of patients can also aid in analyzing the effectiveness of alternative treatments and the development of new treatments, benefitting all patients.

Ìý

Problem

Ever increasing amounts of electronic clinical data and medical subspecialization hinder the ability of doctorsÌýand patientsÌýto stay on top of all aspects of a patient’s medical history.

Solution

Natural Language Processing can automatically process thousands of patient records in seconds. This allows automatic identification of salient diseases, signs, symptoms, and treatments, while preserving the timeline of the patient’s medical history.

Techniques Used

  • Annotation of Temporal Relations Between Events
  • Annotation and Parsing of Abstract Meaning Representations
  • Coreference Annotation and ResolutionÌý
  • Entity & Event Annotation & Recognition

Ìý

Ìý

Ìý ÌýOngoing

Jan 28th

iSAT

Institute for Student-AI Teaming

Project leads

ÌýÌý

Martha Palmer


Martha Palmer

Katharina Kann

Katharina KannÌý

Ìý

Jim Martin
ÌýÌý Ìý Ìý
JimÌýMartinÌý Ìý Ìý ÌýÌý

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alessandro roncone

Alessandro Roncone

Our goal is to use Artificial Intelligence to transform classrooms into more effective, engaging and equitable learning environments.

Ìý

Problem

Â鶹ÒùÔº learn most effectively in collaborative situations where they can investigate and articulate questions about new topics. Break-out groups facilitate an environment where this is possible, however, one teacher can’t engage with several breakout groups simultaneously.

Solution

We are developing new approaches to how machines process human language, gestures and emotions so that we can place an effective AI Partner in each break-out group. It will support the group learning process and provide feedback to the teacher by listening to, analyzing and facilitating problem solving.

Techniques Used

  • Deep learning
  • Reinforcement Learning
  • Speech Recognition
  • Dialogue Management
  • Content Analysis
  • Langauge Modeling
  • Transfer Learning
  • Multimodal User Awareness Modeling

Ìý

Ìý

ÌýÌý Ìý

Ìý ÌýOngoing

Jan 28th

Universal NLP

ÌýÌý

Project leads

Professor Kann

Katharina Kann

Alexis Palmer

Alexis PalmerÌý Ìý Ìý ÌýÌý

Ìý

Ìý

NLP is making immense contributions to theÌýEnglish andÌýChinese speaking worlds. Automating teaching to give children access to education and automatic machine translation increasing access to healthcare areÌýjust two examples.ÌýFor the rest of the world to benefit from NLP, it needs to function in their languagesÌýtoo.

Ìý

Problem

The majority of the world'sÌý7000Ìýlanguages have limited data available for Natural Language Processing.

Solution

When we don’t have enough data to use classical NLP, there are approaches that can make up for this lack.

Techniques Used

- Transfer LearningÌý
- Pre-trainingÌý
- Multi-task TrainingÌý
- Meta Learning