[1] 黄水清,王东波.新时代人民日报分词语料库构建、性能及应用(一)——语料库构建及测评[J]. 图书情报工作, 2019,63(22):5-12.
[2] ZHENG X,CHEN H,XU T.Deep learning for Chinese word segmentation and POS tagging[C]//YAROWSKY D, BALDWIN T, KORHONEN A, et al. Proceedings of the 2013 Conference on empirical methods in natural language processing. Washington:Association for computational Linguistics, 2013:647-657.
[3] LI X, MENG Y, SUN X, et al. Is word segmentation necessary for deep learning of Chinese representations?[J].[2019-11-10]. https://arxiv.org/abs/1508.01991v1.
[4] 张洪刚,李焕.基于双向长短时记忆模型的中文分词方法[J].华南理工大学学报(自然科学版),2017(3):61-67.
[5] MA J, GANCHEV K, WEISS D. State-of-the-art Chinese word segmentation with Bi-LSTMs[C]//RILOFF E, CHIANG D, HOCKENMAIER J, et al. Proceedings of the 2018 conference on empirical methods in natural language processing. Belgium:Association for Computational Linguistics, 2018:4902-4908.
[6] 解宇涵. 基于深度学习的中文分词模型应用研究[D].重庆:重庆大学,2017.
[7] 李雪莲,段鸿,许牧.基于门循环单元神经网络的中文分词法[J].厦门大学学报(自然科学版),2017,56(2):237-243.
[8] 姜猛, 王子牛, 高建瓴. 基于异构数据联合训练的中文分词法[J]. 电子科技, 2019, 32(4):33-36.
[9] 王玮. 基于Bi-LSTM-6Tags的智能中文分词方法[J]. 计算机应用, 2018, 38(S2):112-115.
[10] WANG X, WANG M, ZHANG Q. Realization of Chinese word segmentation based on deep learning method[C]//Green Energy and Sustainable Development I. Proceedings of the international conference on green energy and sustainable development. Chongqing:AIP Publishing, 2017:1-6.
[11] 王梦鸽.基于深度学习中文分词的研究[D]. 西安:西安邮电大学, 2018.
[12] 薛源. 基于深度学习算法的中文分词的研究[J]. 计算机产品与流通, 2019(5):202.
[13] 张子睿,刘云清.基于BI-LSTM-CRF模型的中文分词法[J].长春理工大学学报(自然科学版),2017,40(4):87-92.
[14] 刘玉德. 基于深度学习的中文分词方法研究[D].广州:华南理工大学,2018.
[15] RUMELHART D E, HINTON G E, WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088):533-536.
[16] HUANG Z, XU W, YU K. Bidirectional LSTM-CRF models for sequence tagging[EB/OL].[2019-11-10]. http://arxiv.org/abs/1508.01991v1. |