Context-rule Model for Pos Tagging
Yu-Fang Tsai, Keh-Jiann Chen · Institutional Repositories DataBase (IRDB) · 2003
Part-of-speech tagging for a large corpus is a labour intensive and time-consuming task. In order to achieve fast and high quality tagging, algorithms should be high precision and in particular, its tagging results should require less manual proofreading. In this paper, we proposed a context-rule model to achieve both the above goals for pos tagging. We compared the tagging precisions between Markov bi-gram model and context-rule classifier. According to the experiments, context-rule classifier performs better than those two other algorithms. Also, it covers the data sparseness problem by utilizing more context features, and reduces the amount of corpus that is need to be manual proofread by introducing the confidence measure. 1