实验室硕士研究生关逸飞同学的论文“Understanding Lexical Features for Chinese Essay Grading”（作者：关逸飞、谢翌、刘潇月、孙宇清、龚斌）被ChineseCSCW2019会议录用。

第14届全国计算机支持的协同工作与社会计算学术会议(Chinese Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2019) 将于2019年8月16 日 - 18 日在云南昆明市举行。会议由中国计算机学会（CCF）主办，协同计算专业委员会（CCF TCCC）和昆明理工大学共同承办。作为协同与社会计算领域最重要的全国性学术组织，CCF TCCC已逐步发展并凝练出CSCW、社会计算、群智协同、类人智能协同、流程与服务、协同设计和协同应用为代表的7个代表性研究方向。

ABSTRACT:Essay grading is an important and difficult task in natural language processing. Most of the existing works focus on grading non-native English essays, such as essays in TOEFL. However, these works are not applicable for Chinese essays due to word segmentation and different syntax features. Considering lexical features are important for essay grading, in this paper, we study the expertevaluation standard and propose an interpretable lexical grading method for essays. We first study different levels of vocabulary provided by experts and introduce a quantitative evaluation framework on lexical features. Based on these standards, we quantify the Chinese essay dataset of 12 education grades in primary and middle schools and propose a set of interpretable features. Then a BiLSTM network model is proposed for semantically grading essay, which accepts a sequence of word vectors as input and integrates attention mechanism in terms of lexical richness. We evaluate our method on real datasets and the experimental results show that it outperforms other methods on the task of lexically Chinese essay grading. Besides, our method gives interpretable results, which are helpful for practical applications.