自动化学报2017，Vol.43Issue(11)：1984-1992,9.DOI:10.16383/j.aas.2017.c160330
基于深度学习的维吾尔语名词短语指代消解
Coreference Resolution of Uyghur Noun Phrases Based on Deep Learning
摘要
Abstract
Aimed at the reference phenomena of Uyghur noun phrases, a method using stacked autoencoder model to achieve coreference resolution based on semantic characteristics is presented. Through the study of noun phrases referentiality, we pick up beneficial 13 features for coreference resolution tasks. In order to improve the expression of features for semantic text, Word embedding is added into feature sets, which makes feature sets contain lexical semantic information and context positional relationship. A deep learning algorithm is proposed for unsupervised detection of implicit semantic information, and also introduced is a softmax classifier to decide whether the two markables actually corefer. Experiments show that precision rate, recall rate and F value of coreference resolution reach 74.5 %, 70.6 % and 72.4 %,respectively,which demonstrates that the proposed method on coreference resolution of Uyghur noun phrase and introduction of Word embedding to feature sets are able to improve the performance of coreference resolution system.关键词
深度学习/栈式自编码神经网络/指代消解/Word embedding/维吾尔语Key words
Deep learning/stacked autoencoder/coreference resolution/word embedding/Uyghur引用本文复制引用
李敏,禹龙,田生伟,吐尔根·依布拉音,赵建国..基于深度学习的维吾尔语名词短语指代消解[J].自动化学报,2017,43(11):1984-1992,9.基金项目
国家自然科学基金(61563051,61262064,61662074,61331011),自治区科技人才培养项目(QN2016YX0051) 资助 Supported by National Natural Science Foundation of China (61563051, 61262064, 61662074, 61331011) and Re-gional Scientific and Technological Personnel Training Project (QN2016YX0051) （61563051,61262064,61662074,61331011）
