Using WordNet Based Word Sense Disambiguation to Improve MT Performance
We report on a series of experiments aimed at improving the machine translation of ambiguous lexical items by using WordNet based unsupervised Word Sense Disambiguation (WSD) and comparing its results to three MT systems. Our experiments are performed for the English Slovene language pair using UKB, a freely available graph based word sense disambiguation system. Since the fine granularity of WordNet is often reported as problematic, we compare the performance of UKB using all WordNet senses with using sense clusters. Results are evaluated in three ways: a manual evaluation of WSD performance from MT perspective, an analysis of agreement between the WSD proposed equivalent and those suggested by the three systems, and finally by computing BLEU, NIST and METEOR scores for all translation versions. Our results show that WSD performs with a MT relevant precision of 71 and that 21 of sense related MT errors could be prevented by using unsupervised WSD. We also show that sense clusters improve MT relevant precision.