Learning to Jointly Predict Ellipsis and Comparison Structures
Omid Bakhshandeh, Alexis Wellwood, James F. Allen · 2016
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 interconnected 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.