Year: 2007
Type: article
Abstract: Supervised machine learning algorithms for information extraction generally require large amounts of training data. In many cases where labeling training data is burdensome, there may, however, already exist an incomplete database relevant to the task at hand. Records from this database can be used to label text strings that express the same information. For tasks where text strings do not follow the same format or layout, and additionally may contain extra information, labeling the strings completely may be problematic. This paper presents a method for training extractors which fill in missing labels of a text sequence that is partially labeled using simple high-precision heuristics. Furthermore, we improve the algorithm by utilizing labeled fields from the database. In experiments with BibTeX records and research paper citation strings, we show a significant improvement in extraction accuracy over a baseline that only relies on the database for training data.
Authors: Kedar Bellare, Andrew McCallum
Institution: University of Massachusetts Amherst
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2282396980