Spreading semantic information by Word Sense Disambiguation

Abstract

This paper presents an unsupervised approach to solve semantic ambiguity based on the integration of the Personalized PageRank algorithm with word-sense frequency information. Natural Language tasks such as Machine Translation or Recommender Systems are likely to be enriched by our approach, which includes semantic information that obtains the appropriate word-sense via support from two sources: a multidimensional network that includes a set of different resources (i.e. WordNet, WordNet Domains, WordNet Affect, SUMO and Semantic Classes); and the information provided by word-sense frequencies and word-sense collocation from the SemCor Corpus. Our series of results were analyzed and compared against the results of several renowned studies using SensEval-2, SensEval-3 and SemEval-2013 datasets. After conducting several experiments, our procedure produced the best results in the unsupervised procedure category taking SensEval campaigns rankings as reference.

DOI: 10.1016/j.knosys.2017.06.013

Cite this paper

@article{GutirrezVzquez2017SpreadingSI, title={Spreading semantic information by Word Sense Disambiguation}, author={Yoan Guti{\'e}rrez-V{\'a}zquez and Sonia V{\'a}zquez and Andr{\'e}s Montoyo}, journal={Knowl.-Based Syst.}, year={2017}, volume={132}, pages={47-61} }