Paper: Unsupervised Discovery of Domain-Specific Knowledge from Text

ACL ID P11-1147
Title Unsupervised Discovery of Domain-Specific Knowledge from Text
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

Learning by Reading (LbR) aims at enabling machines to acquire knowledge from and rea- son about textual input. This requires knowl- edge about the domain structure (such as en- tities, classes, and actions) in order to do in- ference. We present a method to infer this im- plicit knowledge from unlabeled text. Unlike previous approaches, we use automatically ex- tracted classes with a probability distribution over entities to allow for context-sensitive la- beling. From a corpus of 1.4m sentences, we learn about 250k simple propositions about American football in the form of predicate- argument structures like “quarterbacks throw passes to receivers”. Using several statisti- cal measures, we show that our model is able to generalize and explain the data statistically significantly bett...