Huge Automatically Extracted Training Sets for Multilingual Word Sense Disambiguation
Table 4: F1 of IMS trained on Train-o-Matic, OMSTI and SemCor, and MFS for the Senseval-2, Senseval-3, SemEval-07, SemEval-13 and SemEval-15 datasets.
['Dataset', 'Train-o-Matic', 'OMSTI', 'SemCor', 'MFS']
SemCor, instead, provides better training data for 3 out of 5 datasets, while the performance of IMS is comparable on the SemEval-2013 and SemEval-2015. This shows that our automatically generated data can lead to better performance than semi-automatic datasets and, in some situations, even surpass that of manually annotated ones. More interestingly, the ability to automatically generate high-quality sense-annotated data enables the creation of domain-specific datasets that could be used to train WSD systems on particular domains of interest. Given that such a system would most likely outperform a system trained on non-specialized data