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.