Paper: Reading to Learn: Constructing Features from Semantic Abstracts

ACL ID D09-1100
Title Reading to Learn: Constructing Features from Semantic Abstracts
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

Jacob Eisenstein∗ James Clarke† Dan Goldwasser† Dan Roth∗† ∗Beckman Institute for Advanced Science and Technology, †Department of Computer Science University of Illinois Urbana, IL 61801 {jacobe,clarkeje,goldwas1,danr}@illinois.edu Abstract Machine learning offers a range of tools for training systems from data, but these methods are only as good as the underly- ing representation. This paper proposes to acquire representations for machine learn- ing by reading text written to accommo- date human learning. We propose a novel form of semantic analysis called read- ing to learn, where the goal is to obtain a high-level semantic abstract of multi- ple documents in a representation that fa- cilitates learning. We obtain this abstract through a generative model that requires no l...