Qing Zhang


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基于话头话体共享结构信息的机器阅读理解研究(Rearch on Machine reading comprehension based on shared structure information between Naming and Telling)
Yujiao Han (韩玉蛟) | Zhiyong Luo (罗智勇) | Mingming Zhang (张明明) | Zhilin Zhao (赵志琳) | Qing Zhang (张青)
Proceedings of the 21st Chinese National Conference on Computational Linguistics

“机器阅读理解(Machine Reading Comprehension, MRC)任务旨在让机器回答给定上下文的问题来测试机器理解自然语言的能力。目前,基于大规模预训练语言模型的神经机器阅读理解模型已经取得重要进展,但在涉及答案要素、线索要素和问题要素跨标点句、远距离关联时,答案抽取的准确率还有待提升。本文通过篇章内话头话体结构分析,建立标点句间远距离关联关系、补全共享缺失成分,辅助机器阅读理解答案抽取;设计和实现融合话头话体结构信息的机器阅读理解模型,在公开数据集CMRC2018上的实验结果表明,模型的F1值相对于基线模型提升2.4%,EM值提升6%。”


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Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric Bayesian Perspective
Qing Zhang | Houfeng Wang
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

For the task of relation extraction, distant supervision is an efficient approach to generate labeled data by aligning knowledge base with free texts. The essence of it is a challenging incomplete multi-label classification problem with sparse and noisy features. To address the challenge, this work presents a novel nonparametric Bayesian formulation for the task. Experiment results show substantially higher top precision improvements over the traditional state-of-the-art approaches.


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Collaborative Topic Regression with Multiple Graphs Factorization for Recommendation in Social Media
Qing Zhang | Houfeng Wang
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers