A Synergistic Approach to Extraction, Learning and Reasoning for Machine Reading

Artificial intelligence researchers have long been attracted by the possibility of designing systems that can learn by reading textual information. In order to meet this goal a system must be able to (1) extract information from text, (2) learn rules that fuse together different pieces of information to make novel inferences and (3) reason about which extracted and inferred facts are true. The goal of this project is to explore a unified "machine reading" system.