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Natural Language Processing Executive Summary



Natural Language Processing Executive Summary

Name: Natural Language Processing (NLP)
Elevator Pitch: Software, dictionaries & know how for computers to understand the meaning of naturally occurring speech and text
Departments: Department of Linguistics & Computer Science & Computational Language & Education Research, University of Colorado

To Indicate an Interest or for More Information: 303.444.2111 or Eric@InnovationCenteroftheRockies.com. Please include a copy of your resume.

Please click here for a PDF of the Natural Language Processing Executive Summary

Background
Natural Language processing is becoming ubiquitous in everyday life. Applications once thought to be farfetched are now within reach as processing power, machine learning algorithms and the internet extend to all facets of everyday life. All of these applications start with a computer’s ability to understand the meaning behind the words.

The Center for Computational Language & EducAtion Research (CLEAR) at the University of Colorado is focused on advancing Human Language Technology and applying it to practical applications. The CLEAR Center goal is the translation of basic research in the area of computational semantics (i.e., getting computers to grasp the meaning of naturally occurring speech and text) into practical systems that can, for example, answer questions given access to texts containing likely answers. In these projects, the faculty apply supervised machine learning techniques to process text to identify entities, events, and the relations between them.

One of the initial research areas has been in the medical field. The faculty researchers are collaborating with Harvard Children’s Hospital and the Mayo Clinic to develop systems for the semantic annotation of medical texts, both web-based resources such as Medpedia and patient clinical reports dictated by physicians. The semantic annotations are used to support two applications, medical domain question answering and constructing event timelines from medical reports.

Technology Benefits
•Processes for developing high quality annotated data at a low cost
•Expertise in applying state-of-the-art machine learning techniques to train high performance automatic systems on the data
•Experience with integrating the individual components into end-to-end systems for particular applications, i.e., domain question answering, constructing event timelines from medical reports
•Pre-existing domain specific annotated data for medical domain

Keywords
Natural language processing, electronic medical records, semantic search, medical billing & coding, web advertising, machine learning

To Indicate an Interest or for More Information: 303.444.2111 or Eric@InnovationCenteroftheRockies.com. Please include a copy of your resume.