Computer Science Cornell University / College Park / University of Maryland / /
IndustryTerm
text categorization algorithm / government-wide web portals / web content / machine learning algorithms / hierarchical categorization algorithm / text categorization algorithms / planned transportation initiatives / committee algorithm / annotation tool / law students / local transportation services / e-postcard / e-rulemaking / law student annotators / feasible solution / upper-level law students / information technology / natural language processing / law firms / natural language processing methods / learning algorithms / /
Organization
National Science Foundation / University of Maryland / College Park / Cornell University / Adil Aijaz Faculty of Computing and Information Science / federal government / Law School / Department of Commerce / Federal Transit Authority / Department of Transportation / Stanford / Association for Computational Linguistics / /
Person
Susan Dumais / Massimiliano Pontil / Sarah Zelikovitz / Claire Cardie / Cynthia Farina / Prasad Tadepalli / Matt Rawding / Mehran Sahami / Rich Caruana / Namhee Kwon / Kristin Bennett / E LIG ACTIV / Adil Aijaz / Morgan Kaufmann / Daphne Koller / Eduard Hovy / Stuart Russell / Hao Chen / Goekhan Bakir / Timothy D. Heilman / Haym Hirsh / Stephen Purpura / Danny Silver / Stuart Shulman / Jesse Simons / /
An eRulemaking Corpus: Identifying Substantive Issues in Public Comments Claire Cardie, Cynthia Farina, Matt Rawding, and Adil Aijaz Faculty of Computing and Information Science, Law School, Information Science, Dept. of