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.
Cite as: Poon, H. (2012) Unsupervised semantic parsing. Proc. Machine Learning in Speech and Language Processing (MLSLP 2012)
@inproceedings{poon12_mlslp,
author={Hoifung Poon},
title={{Unsupervised semantic parsing}},
year=2012,
booktitle={Proc. Machine Learning in Speech and Language Processing (MLSLP 2012)}
}