Paper: Learning Constraints for Consistent Timeline Extraction

ACL ID D12-1080
Title Learning Constraints for Consistent Timeline Extraction
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012
Authors

We present a distantly supervised system for extracting the temporal bounds of fluents (re- lations which only hold during certain times, such as attends school). Unlike previous pipelined approaches, our model does not as- sume independence between each fluent or even between named entities with known con- nections (parent, spouse, employer, etc.). In- stead, we model what makes timelines of flu- ents consistent by learning cross-fluent con- straints, potentially spanning entities as well. For example, our model learns that someone is unlikely to start a job at age two or to marry someone who hasn?t been born yet. Our sys- tem achieves a 36% error reduction over a pipelined baseline.