An Effective Approach to Biomedical Information Extraction with Limited Training Data

TL;DR

This paper presents a novel method combining sentence simplification and distributional semantics to improve biomedical information extraction with limited training data, offering a versatile semi-supervised approach.

Contribution

It introduces the first integration of distributional semantics with information extraction, enhancing semi-supervised learning in biomedical text analysis.

Findings

Effective concept extraction using combined methods

Applicable across various frameworks and algorithms

Improved extraction accuracy with limited data

Abstract

Overall, the two main contributions of this work include the application of sentence simplification to association extraction as described above, and the use of distributional semantics for concept extraction. The proposed work on concept extraction amalgamates for the first time two diverse research areas -distributional semantics and information extraction. This approach renders all the advantages offered in other semi-supervised machine learning systems, and, unlike other proposed semi-supervised approaches, it can be used on top of different basic frameworks and algorithms. http://gradworks.umi.com/34/49/3449837.html