融合分词隐层特征的汉语基本块识别

该文以字为基本标注单位,构建了一种汉语基本块识别的神经网络学习模型。模型联合分词任务的神经网络学习模型与基本块识别任务模型,将分词任务模型中学习得到的隐层特征融入基本块识别的模型中,两模型相互交替优化学习模型参数,并实现了以整句似然函数(而非单字似然函数)作为优化目标的算法。实验结果表明:1)以整句似然函数为优化目标的基本块识别的F值比单字似然情形要高出1.33%,特别是在多字块识别中,其召回率比单字似然情形要高出4.68%;2)融合分词任务模型中的隐层特征的汉语基本块识别模型的结果比不做融合的模型要高出2.17%,说明融合分词隐层特征的交替联合学习方法是有效的。

Chinese Base-Chunk Identification Using Hidden-Layer Feature of Segmentation

Based on the unit of Chinese character, a neural network learning model for Chinese base-chunk identification is constructed. The model combines the neural network learning model of segmentation task with the model of base-chunk identification by using the hidden-layer features of segmentation. The sentence-level likelihood function for base-chunk identification task is employed as the optimization target. The parameters of the two learning model are trained in turn. The experimental results show that: 1) the F-score of base-chunk identification with sentence-level likelihood function is 1.33% higher than that with character-level likelihood function, and especially, the recall for the multi-characters chunk identification is improved as much as 4.68%. 2) The final result of using hidden-layer features of segmentation task is 2.17% higher.