利用源域结构的粒迁移学习及词性标注应用

摘要

迁移学习在一定程度上减轻了目标域的数据稀疏问题对泛化能力的影响,然而泛化能力的提高仍然受到负迁移等问题的影响.为了解决负迁移问题,该文提出使用源域结构的文本语料的信息粒化方法,用区间信息粒表示出源域数据集的结构对数据集中统计量的影响.然后提出区间二型模糊隐马尔可夫模型(Interval Type-2 fuzzy Hidden Markov Model,IHMM)以处理区间信息粒.给出了IHMM的构建方法和去模糊化方法.在文本的词性标注任务中进行了多个实验,可以证实利用源域结构信息的粒迁移学习方法避免了负迁移,提高了模型的泛化能力.%Transfer learning alleviates the data sparseness issue to some extent,but the generalization capacity is still hindered by negative-transfer problem.To address this issue,we propose an information granulation method for text corpora based on source domain structure.Interval granules are employed to express the influence of source domain structure on statistics of the dataset.We further design an Interval Type-2 fuzzy Hidden Markov Model (IHMM) to deal with the interval granules.Experiments on part-of-speech tagging proves that the proposed method avoids negative-transfer and improves generalization capacity.