Unsupervised Semantic Parsing

Hoifung Poon and Pedro Domingos


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.


Paper (PDF)
USP code (released under the Modified BSD License.)
User guide

Datasets used:

Answers extracted by USP (with manual labels)