Kernel Fuzzy C-Means Clustering for Word Sense Disambiguation in BioMedical Texts
K. REN, Y.F. REN
Journal of Digital Information Management, Vol. 13, No. 6, 2015.
DOI: 
Link: http://dline.info/fpaper/jdim/v13i6/v13i6_2.pdf
Abstract:
Word sense disambiguation (WSD) in biomedical
texts is important. The majority of existing research
primarily focuses on supervised learning methods
and knowledge-based approaches. Implementing these
methods requires significant human-annotated corpus,
which is not easily obtained. In this paper, we developed
an unsupervised system for WSD in biomedical texts.
First, we predefine the number of senses for an ambiguous
word. Kernel fuzzy C-means clustering is used to
group the same sense terms into a set. Each set is
mapped to a certain sense to achieve disambiguation.
Experimental results on all 50 ambiguous terms from NLMWSD
corpus demonstrate that our proposed system outperforms
other unsupervised methods. Meanwhile, the
kernel fuzzy C-means system is 5% more precise than
the state-of-art knowledge-based WSD system on the
full NLM-WSD dataset. Our system is highly efficient and
accurate for word sense disambiguation in biomedical
texts and does not require human-annotated corpus.