NLP @ CU Boulder

The NLP Process

Training computers to accurately deal with languages is a complex process that intricately weaves together linguistic insights and computational models that reference real world contexts. The process can begin with linguistic analysis, computational models, or a combination of the two. After it’s begun, however, it usually cycles in the following manner.

An infographic describing the NLP process

The NLP Ecosystem

The NLP ecosystem is comprised of linguists, computer scientists, and domain experts, as well as the computational linguists who link these three groups together.

An infographic about the NLP ecosystem

If this entire process seems interesting to you, why not become a computational linguist?

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Featured Projects

Our faculty are engaged in research projects ranging from language documentation and morphological analysis to semantic analysis and 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 the projects we are most proud of, both past and present.

Ongoing

Jan 28th

DARPA AIDA Program

Autonomous Interperation of Disparate Alternatives

Project leads

Martha Palmer

Susan Brown

Jim Martin

Chris Heckman

Our goal is to automatically analyze the content of written documents and extract key pieces of information about the events they describe, including where different 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, to help make our job of keeping on top of things much more tractable.

Techniques Used

Deep Learning
Graph Embeddings
Coreference Resolution
Type Matching
Entity & Event Annotation & Recognition
Ontology Construction & Mapping

Ongoing

Jan 28th

THYME

Temporal History of Your Medical Events

Project leads

Martha Palmer

Jim Martin

Kristin Wright-Bettener

Kristin Wright-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.

Problem

Ever increasing amounts of electronic clinical data and medical subspecialization hinder the ability of doctors and patients to 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 Resolution
Entity & Event Annotation & Recognition

Ongoing

Jan 28th

Universal NLP

Project leads

Katharina Kann

Alexis Palmer

NLP is making immense contributions to the English and Chinese speaking worlds. Automating teaching to give children access to education and automatic machine translation increasing access to healthcare are just two examples. For the rest of the world to benefit from NLP, it needs to function in their languages too.

Problem

The majority of the world's 7000 languages 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 Learning
- Pre-training
- Multi-task Training
- Meta Learning

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Project leads

Problem

Solution

Techniques Used

Project leads

Problem

Solution

Techniques Used