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, и G. Paass. KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, стр. 773--782. New York, NY, USA, ACM, (2010)
F. Reichartz, H. Korte, и G. Paass. Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, стр. 365--368. Suntec, Singapore, Association for Computational Linguistics, (августа 2009)