tailieunhanh - Báo cáo khoa học: "Bilingual Co-Training for Monolingual Hyponymy-Relation Acquisition"

This paper proposes a novel framework called bilingual co-training for a largescale, accurate acquisition method for monolingual semantic knowledge. In this framework, we combine the independent processes of monolingual semanticknowledge acquisition for two languages using bilingual resources to boost performance. We apply this framework to largescale hyponymy-relation acquisition from Wikipedia. | Bilingual Co-Training for Monolingual Hyponymy-Relation Acquisition Jong-Hoon Oh Kiyotaka Uchimoto and Kentaro Torisawa Language Infrastructure Group MASTAR Project National Institute of Information and Communications Technology NICT 3-5 Hikaridai Seika-cho Soraku-gun Kyoto 619-0289 Japan rovellia uchimoto torisawa @ Abstract 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 . 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. 1 Motivation Acquiring and accumulating semantic knowledge are crucial steps for developing high-level NLP applications such as question answering although it remains difficult to acquire a large amount of highly accurate semantic knowledge. This paper proposes a novel framework for a large-scale accurate acquisition method for monolingual semantic knowledge especially for semantic relations between nominals such as hyponymy and meronymy. We call the framework bilingual cotraining. The acquisition of semantic relations between nominals can be seen as a classification task of semantic relations - to determine whether two nom-inals hold a particular semantic relation Girju et al. 2007 . Supervised learning methods which have often been applied to this classification task have shown promising results. In those methods however a large amount of training data is usually required to obtain high performance and the high costs of preparing