Paper: Unsupervised Semantic Parsing

ACL ID D09-1001
Title Unsupervised Semantic Parsing
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

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 recur- sively assigning its parts to lambda-form clusters and composing them. We evalu- ate our approach by using it to extract a knowledge base from biomedical abstracts and answer questions. USP substantially outperforms TextRunner, DIRT and an in- formed baseline on both precision and re- call on this task.