Portrait of Lucy Vanderwende

Lucy Vanderwende

Senior Researcher

About

Lucy holds a Ph.D. in Computational Linguistics from Georgetown University, in Washington D.C. Lucy worked at IBM Bethesda on natural language processing from 1988 – 1990. In 1991, she was a Visiting Scientist at the Institute for Systems Science in Singapore.  Lucy has worked at Microsoft Research since 1992.  Lucy was Program Co-Chair for NAACL in 2009 and General Chair for NAACL in 2013. Since 2011, Lucy is also Affiliate Associate Faculty at University of Washington Department of Biomedical Health Informatics, a member of the UW BioNLP group, who are using NLP technology to extract critical information from patient reports.

Research Interests

MindNet: automated acquisition of semantic knowledge
Summarization, focusing on summary generation and evaluation
Making reading more effective
Question Generation
Computer-Assisted Grading
NLPwin, robust, broad-coverage language analysis at Microsoft
NLP and Healthcare

 

Projects

Publications

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Lucy’s research focuses on text understanding. She is deeply involved with developing MindNet, a method for automatically acquiring semantic information. All types of semantic information can be identified in and extracted from text. Dictionaries can provide the semantic information, for example, that a sheep is an animal; encyclopedias provide specific knowledge, for example, that Armstrong landed on the moon. Specialized data sets provide information on a given topic, for example, that Microsoft Research was founded in 1991. Common sense information can also be extracted from web-scale resources. Such information can be extracted in a variety of ways, from rule-based to completely unsupervised.

Lucy’s focus is to work with applications that demonstrate how the information in a knowledge resource can be used to improve human understanding and productivity.  In particular, she has been involved in several projects in Healthcare that are aimed at understanding and structuring the information contained in unstructured text such as a patient’s clinical records (e.g., for phenotype prediction) or biomedical scientific publications. Understanding the author’s commitment to the reliability of the statement (sometimes called, assertion detection) is key to providing a robust understanding of the text.

Lucy is also excited to be working on ways to make reading more effective. One avenue is to support a reader’s mastery of the text by using Question Generation to create quizzes for arbitrary selections of text. With such quizzes, the reader can see for themselves which part(s) of the text they know and which they should re-read.  The value of open-response questions to support learning is well-known. She is also working on enabling teachers to pose open-response questions by creating a workflow called Powergrading, where the teacher grades clusters of answers simultaneously, identifies answers that don’t belong in the cluster, and provides rich feedback while gaining insight into how well the students are doing in class.