Improved convolutional neural network for biomedical word sense disambiguation with enhanced context feature modeling
REN Kai , WANG Shi-Wen
Journal of Digital Information Management, Vol. 14, No. 6, 2016.
DOI: 
Link: http://dline.info/fpaper/jdim/v14i6/jdimv14i6_1.pdf
Abstract:
Polysemy is a common phenomenon in the
biomedical domain. Ambiguous words directly influence
the accuracy of computer semantic analyses. Thus, word
sense disambiguation (WSD) is often conducted in
advance. Most current biomedical WSD methods rely on
manual selection of features for WSD. To identify latent
context features from a deep layer and reduce the negative
influence of manual selection of features in WSD, this
paper proposes the Convolutional Neural Network (CNN)
method for biomedical WSD with enhanced text feature
modeling. First, this program automatically conducts
crawling of a large scale of relevant corpus from MEDLINE
for training and obtains relevant context feature vectors.
These feature vectors are subsequently adopted as input
data in CNN. Finally, the CNN classification method is
used for WSD. By testing 203 commonly used ambiguous
words from MSH-WSD corpus, the author finds that the
average accuracy of the proposed method is 94.65%,
which is a significant improvement relative to that of
previous methods. This result proves that CNN is an
efficient WSD method to be used in the biomedical
domain. Given that context feature representation and
WSD are important pre-works in extraction and retrieval
of biomedical information, WSD can reduce the negative
effect of ambiguous words on accuracy of such pre-works.