Learning to Jointly Predict Ellipsis and Comparison Structures

Omid Bakhshandeh1, Alexis Cornelia Wellwood2, James Allen1
1University of Rochester, 2Northwestern University


Domain-independent meaning representation of text has received a renewed interest in the NLP community. Comparison plays a crucial role in shaping objective and subjective opinion and measurement in natural language, and is often expressed in complex constructions including ellipsis. In this paper we introduce a novel framework for jointly capturing the semantic structure of comparison and ellipsis constructions. Our framework models ellipsis and comparison as inter-connected predicate-argument structures, which enables automatic ellipsis resolution. We show that a structured prediction model trained on our dataset of 2,800 gold annotated review sentences yields promising results. Together with this paper we release the dataset and an annotation tool which enables two-stage expert annotation on top of tree structures.