  Shallow Convolution Neural Network (CNN) is a time-tested tool for the
information extraction from cancer pathology reports. Shallow CNN performs
competitively on this task to other deep learning models including BERT, which
holds the state-of-the-art for many NLP tasks. The main insight behind this
eccentric phenomenon is that the information extraction from cancer pathology
reports require only a small number of domain-specific text segments to perform
the task, thus making the most of the texts and contexts excessive for the
task. Shallow CNN model is well-suited to identify these key short text
segments from the labeled training set; however, the identified text segments
remain obscure to humans. In this study, we fill this gap by developing a model
reduction tool to make a reliable connection between CNN filters and relevant
text segments by discarding the spurious connections. We reduce the complexity
of shallow CNN representation by approximating it with a linear transformation
of n-gram presence representation with a non-negativity and sparsity prior on
the transformation weights to obtain an interpretable model. Our approach
bridge the gap between the conventionally perceived trade-off boundary between
accuracy on the one side and explainability on the other by model reduction.
