Paper: Bootstrapping Information Extraction from Field Books

ACL ID D07-1087
Title Bootstrapping Information Extraction from Field Books
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2007
Authors

We present two machine learning ap- proaches to information extraction from semi-structured documents that can be used if no annotated training data are available, but there does exist a database filled with information derived from the type of docu- ments to be processed. One approach em- ploys standard supervised learning for infor- mation extraction by artificially constructing labelled training data from the contents of the database. The second approach com- bines unsupervised Hidden Markov mod- elling with language models. Empirical evaluation of both systems suggests that it is possible to bootstrap a field segmenter from a database alone. The combination of Hid- den Markov and language modelling was found to perform best at this task.