Unsupervised Semantic Parsing
Hoifung Poon and Pedro Domingos
Abstract:
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.
Download:
Paper (PDF)
USP code (released under the Modified BSD License.)
User guide
Datasets used:
Questions
Answers extracted by USP (with manual labels)
GENIA