1 research outputs found
A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction
Textual patterns (e.g., Country's president Person) are specified and/or
generated for extracting factual information from unstructured data.
Pattern-based information extraction methods have been recognized for their
efficiency and transferability. However, not every pattern is reliable: A major
challenge is to derive the most complete and accurate facts from diverse and
sometimes conflicting extractions. In this work, we propose a probabilistic
graphical model which formulates fact extraction in a generative process. It
automatically infers true facts and pattern reliability without any
supervision. It has two novel designs specially for temporal facts: (1) it
models pattern reliability on two types of time signals, including temporal tag
in text and text generation time; (2) it models commonsense constraints as
observable variables. Experimental results demonstrate that our model
significantly outperforms existing methods on extracting true temporal facts
from news data.Comment: 7 pages, 1 figur