Source code to repeat the paper evaluation: We present the first unsupervised approach to the problem of learning a semantic parser, using Markov logic. Our USP system transforms dependency trees into quasi-logical forms, recursively induces lambda forms from these, and clusters them to abstract away syntactic variations of the same meaning. The MAP semantic parse of a sentence is obtained by recursively assigning its parts to lambda-form clusters and composing them. We evaluate our approach by using it to extract a knowledge base from biomedical abstracts and answer questions. USP substantially outperforms TextRunner, DIRT and an informed baseline on both precision and recall on this task.
F. Reichartz, H. Korte, and G. Paass. KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, page 773--782. New York, NY, USA, ACM, (2010)
F. Reichartz, H. Korte, and G. Paass. Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, page 365--368. Suntec, Singapore, Association for Computational Linguistics, (August 2009)