中国英语学习者人数众多，迫切需要针对中国学生特点的、有效适用于大规模英文作文数据的全自动评分算法，以解决中国现有英语教学和大规模英语考试中英文作文批改量大和难度大的瓶颈问题。现有作文特征提取和自动评分技术主要面向以英语为母语的学生作文，针对中国英语学习者的全自动作文评分及诊断反馈技术的研究成果还不多见。本项目主要进行以下几方面的研究：(1)研究能够呈现中国学生英文写作特点的特征提取算法；(2)针对作文分数具有不平衡分布的特点，研究基于不平衡数据有效分类的作文自动评分算法；(3)研究基于增量聚类的作文自动评分算法；(4)研究基于多文档主题词提取的作文离题识别算法；(5)研究不依赖于大规模作文错误语料库的语法检错及正确推荐算法。研究内容同时将推动中文作文自动评分、小语种作文自动评分以及英语主观题自动评分等相关场景中的应用。

As we all know, there are a large number of Chinese English learners in China. Teachers need to correct large amounts of English essays and educational institutions had to assess large-scale English testing essays every year. The substantial quantities and great difficulties in English writing assessment is now the bottleneck problem in English teaching and testing. In order to solve the problem, effective automated essay scoring and diagnostic feedback algorithms are in great need of in China. The existing composition feature extraction algorithms and automated essay scoring technology are primarily for native English-speaking writers, and few results of research are for non-native English-speaking writers. The purpose of the project is to study and find the effective automated essay scoring algorithms and diagnostic feedback methods for Chinese English learners. In the project, five aspect of research are to be study:.1) First, we will study and present the effective feature extraction methods which can not only reveal the characteristic of Chinese students writing, but also improve the result of scoring for Chinese student’s essays basing on second language acquisition theory..2) Generally, the number of middle score essays are much more than that of high score and low score essays, so the data of essays are imbalance. In the project, a good strategy is to be study to improve the classification result for the imbalance data of essays..3) Basing on the result of our research on clustering algorithm for large-scale text, we are going to study the application of clustering algorithm on the field of automated essay scoring and propose a new scoring algorithm basing on two-stage incremental clustering strategy. .4) On the aspect of off-topic essay detection, the subject word extraction mechanism of multiple documents and language semantic calculation are combined with as the detection method..5) On the aspect of diagnostic feedback, we are mainly focus on grammatical error detection and suggested correction algorithm that is independent of large scale error-marked essay corpus basing on our preliminary test result..The research has important theoretical value and broad application prospects, and could help improve performance in scenario such as automated Chinese essay scoring, minority language automatic essay scoring and English subjective question assessment..