To improve the accuracy of word sense disambiguation (WSD) has been a significant issue, and to visualize the structure of a dataset to discover knowledge has been an urgent demand in natural language processing. In order to fulfill these two tasks simultaneously, a new approach of attribute partial order structure diagram is proposed. The principle of attribute partial order and the approach of attribute partial order structure diagram are described. The proposed approach is testified by the WSD of the English preposition over, using the dataset from SemEval corpus. Two well-accepted sense inventories for fine-grained WSD of the English prepositions are adopted. The formal contexts for the fine-grained WSD of the English preposition over are established and the corresponding attribute partial order structure diagrams are generated and used as the models of WSD. The tested results show that the accuracies of WSD of over by the proposed approach are significantly higher than the ones by the state of the art system. Moreover, the proposed approach can visualize the attribute partial order structure of the dataset, which can be used for knowledge discovery.
['Jianping Yu', 'Wenxue Hong', 'Chengling Qiu', 'Shaoxiong Li', 'Deming Mei']
A new approach of attribute partial order structure diagram for word sense disambiguation of English prepositions