KDSL: a Knowledge-Driven Supervised Learning Framework for Word Sense Disambiguation
Table 4: F1-scores (%) for nouns disambiguation
['Methods', 'Test Datasets SE2', 'Test Datasets SE3', 'Test Datasets SE13', 'Test Datasets SE15']
Among these settings, MFS, OMSTI, Train-O-Matic, DisDict and DisDict+MFS do not require manually labeled data while Weissenborn et al. and MLab+DisDict+ULab+MFS require. For data generation methods which are closet to ours, i.e., OMSTI and Train-O-Matic, we train our supervised model on their generated data and post the results. Compared them with DisDict, we can concludes that our new data generation method beats these two approaches by two points. First, these methods only focus on nouns disambiguation, while our work is appropriate to any POS in English. Second, our generated data has a higher quality with a better overall performance on nouns disambiguation.