Improving morphology induction by learning spelling rules
IJCAI(2009)
摘要
Unsupervised learning of morphology is an important task for human learners and in natural language processing systems. Previous systems focus on segmenting words into substrings (taking ⇒ tak.ing), but sometimes a segmentation-only analysis is insufficient (e.g., taking may be more appropriately analyzed as take+ing, with a spelling rule accounting for the deletion of the stem-final e). In this paper, we develop a Bayesian model for simultaneously inducing both morphology and spelling rules. We show that the addition of spelling rules improves performance over the baseline morphology-only model.
更多查看译文
关键词
human learner,unsupervised learning,important task,baseline morphology-only model,spelling rule accounting,segmentation-only analysis,improving morphology induction,previous system,natural language processing system,bayesian model
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要