Bilingual Co-Training for Monolingual Hyponymy-Relation Acquisition

This paper proposes a novel framework called bilingual co-training for a large-scale, accurate acquisition method for monolingual semantic knowledge. In this framework, we combine the independent processes of monolingual semantic-knowledge acquisition for two languages using bilingual resources to boost performance. We apply this framework to large-scale hyponymy-relation acquisition from Wikipedia. Experimental results show that our approach improved the F-measure by 3.6--10.3%. We also show that bilingual co-training enables us to build classifiers for two languages in tandem with the same combined amount of data as required for training a single classifier in isolation while achieving superior performance.