Yingjie Han


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期货领域知识图谱构建(Construction of Knowledge Graph in Futures Field)
Wenxin Li (李雯昕) | Hongying Zan (昝红英) | Tongfeng Guan (关同峰) | Yingjie Han (韩英杰)
Proceedings of the 21st Chinese National Conference on Computational Linguistics

“期货领域是数据最丰富的领域之一,本文以商品期货的研究报告为数据来源构建了期货领域知识图谱(Commodity Futures Knowledge Graph,CFKG)。以期货产品为核心,确立了概念分类体系及关系描述体系,形成图谱的概念层;在MHS-BIA与GPN模型的基础上,通过领域专家指导对242万字的研报文本进行标注与校对,形成了CFKG数据层,并设计了可视化查询系统。所构建的CFKG包含17,003个农产品期货关系三元组、13,703种非农产品期货关系三元组,为期货领域文本分析、舆情监控和推理决策等应用提供知识支持。”


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Chinese Grammatical Error Diagnosis Based on RoBERTa-BiLSTM-CRF Model
Yingjie Han | Yingjie Yan | Yangchao Han | Rui Chao | Hongying Zan
Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications

Chinese Grammatical Error Diagnosis (CGED) is a natural language processing task for the NLPTEA6 workshop. The goal of this task is to automatically diagnose grammatical errors in Chinese sentences written by L2 learners. This paper proposes a RoBERTa-BiLSTM-CRF model to detect grammatical errors in sentences. Firstly, RoBERTa model is used to obtain word vectors. Secondly, word vectors are input into BiLSTM layer to learn context features. Last, CRF layer without hand-craft features work for processing the output by BiLSTM. The optimal global sequences are obtained according to state transition matrix of CRF and adjacent labels of training data. In experiments, the result of RoBERTa-CRF model and ERNIE-BiLSTM-CRF model are compared, and the impacts of parameters of the models and the testing datasets are analyzed. In terms of evaluation results, our recall score of RoBERTa-BiLSTM-CRF ranks fourth at the detection level.

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Chinese Grammatical Errors Diagnosis System Based on BERT at NLPTEA-2020 CGED Shared Task
Hongying Zan | Yangchao Han | Haotian Huang | Yingjie Yan | Yuke Wang | Yingjie Han
Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications

In the process of learning Chinese, second language learners may have various grammatical errors due to the negative transfer of native language. This paper describes our submission to the NLPTEA 2020 shared task on CGED. We present a hybrid system that utilizes both detection and correction stages. The detection stage is a sequential labelling model based on BiLSTM-CRF and BERT contextual word representation. The correction stage is a hybrid model based on the n-gram and Seq2Seq. Without adding additional features and external data, the BERT contextual word representation can effectively improve the performance metrics of Chinese grammatical error detection and correction.


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Automatic Grammatical Error Detection for Chinese based on Conditional Random Field
Yajun Liu | Yingjie Han | Liyan Zhuo | Hongying Zan
Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)

In the process of learning and using Chinese, foreigners may have grammatical errors due to negative migration of their native languages. Currently, the computer-oriented automatic detection method of grammatical errors is not mature enough. Based on the evaluating task — CGED2016, we select and analyze the classification model and design feature extraction method to obtain grammatical errors including Mission(M), Disorder(W), Selection (S) and Redundant (R) automatically. The experiment results based on the dynamic corpus of HSK show that the Chinese grammatical error automatic detection method, which uses CRF as classification model and n-gram as feature extraction method. It is simple and efficient which play a positive effect on the research of Chinese grammatical error automatic detection and also a supporting and guiding role in the teaching of Chinese as a foreign language.