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

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.