Unsupervised semantic parsing
Hoifung Poon, Pedro Domingos · 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 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.