Abstract
In this paper, we propose a method to detect new word senses of a target word from sentences that contain it. To achieve this, we assume a new word sense sentence as an outlier of a data set constructed by sentences that contain the target word. Then using outlier detection methods in the data mining domain, we detect the new word senses. Generally, outlier detection methods are considered to be unsupervised. However, our method utilises data sets including some sentences with the labelled target word. Therefore, our outlier detection method is classified under the supervised framework. We propose an ensemble method of two methods to detect new word sense sentences: the supervised LOF (Local Outlier Factor) and the supervised generative model. The final output is the intersection of outputs of both methods. We demonstrate the effectiveness of our method using SemEval-2 Japanese WSD task data. Moreover we show that word sense disambiguation systems cannot solve our task by themselves.
