We present a nonparametric Bayesian approach to extract a structured database of entities from text. Neither the number of entities nor the fields that characterize each entity are provided in advance; the only supervision is a set of five prototype examples. Our method jointly accomplishes three tasks: (i) identifying a set of canonical entities, (ii) inferring a schema for the fields that describe each entity, and (iii) matching entities to their references in raw text. Empirical evaluation shows that the approach learns an accurate database of entities and a sensible model of name structure.
More similar to our work, REF introduced a nonparametric Bayesian approach to extract structured databases of entities.
Structured Databases of Named Entities from Bayesian Nonparametrics
Proceedings of the First workshop on Unsupervised Learning in NLP
Computer Science