Symposium on Machine Learning in Speech and Language Processing (MLSLP)
Portland, Oregon, USA
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.
Bibliographic reference. Poon, Hoifung (2012): "Unsupervised semantic parsing", In MLSLP-2012 (abstract).