On the effects of low-quality training data on information extraction from clinical reports

Marcheggiani D., Sebastiani F.

Training data quality  Information extraction  I.2.6 Learning 

In the last five years there has been a flurry of work on information extraction from clinical documents, i.e., on algorithms capable of extracting, from the informal and unstructured texts that are generated during everyday clinical practice, mentions of concepts relevant to such practice. Most of this literature is about methods based on supervised learning, i.e., methods for training an information extraction system from manually annotated examples. While a lot of work has been devoted to devising learning methods that generate more and more accurate information extractors, little work (if any) has been devoted to investigating the effect of the quality of training data on the learning process. Low quality in training data sometimes derives from the fact that the person who has annotated the data is different (e.g., more junior) from the one against whose judgment the automatically annotated data must be evaluated. In this paper we test the impact of such data quality issues on the accuracy of information extraction systems oriented to the clinical domain. We do this by comparing the accuracy deriving from training data annotated by the authoritative coder (i.e., the one who has annotated the test data), with the accuracy deriving from training data annotated by a different coder. The results indicate that, although the disagreement between the two coders (as measured on the training set) is substantial, the difference in accuracy is not so. This hints at the fact that current learning technology is robust to the use of training data of suboptimal quality.

Source: IIR 2014 - 5th Italian Information Retrieval Workshop, Roma, Italy, 20-21 January 2014