Relation Acquisition using Word Classes and Partial Patterns

This paper proposes a semi-supervised rela-tion acquisition method that does not rely on extraction patterns (e.g. “X causes Y ” for causal relations) but instead learns a combi-nation of indirect evidence for the target re-lation — semantic word classes and partial patterns. This method can extract long tail instances of semantic relations like causality from rare and complex expressions in a large JapaneseWeb corpus — in extreme cases, pat-terns that occur only once in the entire cor-pus. Such patterns are beyond the reach of cur-rent pattern based methods. We show that our method performs on par with state-of-the-art pattern based methods, and maintains a rea-sonable level of accuracy even for instances acquired from infrequent patterns. This abil-ity to acquire long tail instances is crucial for risk management and innovation, where an ex-haustive database of high-level semantic rela-tions like causation is of vital importance.