Paper: Empirically-motivated Generalizations of CCG Semantic Parsing Learning Algorithms

ACL ID E14-1037
Title Empirically-motivated Generalizations of CCG Semantic Parsing Learning Algorithms
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Learning algorithms for semantic parsing have improved drastically over the past decade, as steady improvements on bench- mark datasets have shown. In this pa- per we investigate whether they can gen- eralize to a novel biomedical dataset that differs in important respects from the tra- ditional geography and air travel bench- mark datasets. Empirical results for two state-of-the-art PCCG semantic parsers in- dicates that learning algorithms are sensi- tive to the kinds of semantic and syntac- tic constructions used in a domain. In re- sponse, we develop a novel learning algo- rithm that can produce an effective seman- tic parser for geography, as well as a much better semantic parser for the biomedical dataset.