中文作文句间逻辑合理性智能判别方法研究
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摘要
在作文评测中,句间逻辑合理性是评价语言运用能力的一项重要指标。从文本分类的角度,对作文段落句间逻辑合理性进行定性分析。依据逻辑合理的段落其句子的位置是相对固定的,将现有的基于传统、基于深度学习的文本分类算法应用在中小学人物类作文段落句间逻辑合理性的判别上,实验结果表明使用分类模型对段落句间逻辑合理性判别是有效的。在此基础上,进一步提出代表句子特征的关键词筛选方法,实验结果表明使用句首关键词、句尾关键词或两者结合作为句子特征的方法,比句子全部关键词更能代表句子信息,明显提高判别的准确率。
In composition evaluation,logical rationality between sentences is an important indicator to evaluate the ability of language application. From the perspective of text classification,this paper made a qualitative analysis of the logical rationality between sentences of paragraphs. According to the fact that the position of the sentence was relatively fixed in a logical and reasonable paragraph,we applied the existing text classification algorithms based on traditional and deep learning into the discrimination of logical rationality between sentences of paragraphs in character composition in primary and secondary schools. The experimental result shows that it is effective to use the classification model to distinguish logical rationality between sentences of paragraphs. On this basis,the keyword selection method representing the features of the sentence was further proposed. The experimental result shows that the method of using sentence heading keywords,sentence ending keywords or the combination of the two as the sentence features can represent the sentence information better than all the sentence keywords,which significantly improves the accuracy of discrimination.
In composition evaluation,logical rationality between sentences is an important indicator to evaluate the ability of language application. From the perspective of text classification,this paper made a qualitative analysis of the logical rationality between sentences of paragraphs. According to the fact that the position of the sentence was relatively fixed in a logical and reasonable paragraph,we applied the existing text classification algorithms based on traditional and deep learning into the discrimination of logical rationality between sentences of paragraphs in character composition in primary and secondary schools. The experimental result shows that it is effective to use the classification model to distinguish logical rationality between sentences of paragraphs. On this basis,the keyword selection method representing the features of the sentence was further proposed. The experimental result shows that the method of using sentence heading keywords,sentence ending keywords or the combination of the two as the sentence features can represent the sentence information better than all the sentence keywords,which significantly improves the accuracy of discrimination.
引文
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[2]傅间莲,陈群秀.自动文摘系统中的主题划分问题研究[J].中文信息学报,2005,19(6):28-35.
[3]林林.基于规则的文本过滤系统设计与实现[D].广州:华南理工大学,2003.
[4]郑婧,孙卫.国内自然语言处理技术研究与应用的状态[J].数字图书馆论坛,2008(7):27-31.
[5]黄曾阳.HNC理论与自然语言语句的理解[J].中国基础科学,1999(S1):85-90.
[6]黄曾阳.HNC(概念层次网络)理论:计算机理解语言研究的新思路[M].北京:清华大学出版社,1998.
[7]司贝贝,杨进才.基于依存关系的复句关系词搭配库建设[J].Software Engineering&sapplications,2015,4(4):81-87.
[8]李艳翠,孙静,周国栋.汉语篇章连接词识别与分类[J].北京大学学报(自然科学版),2015,51(2):307-314.
[9]杨进才,郭凯凯,沈显君,等.基于贝叶斯模型的复句关系词自动识别与规则挖掘[J].计算机科学,2015,42(7):291-294.
[10]刘挺,车万翔,李正华.语言技术平台[J].中文信息学报,2011,25(6):53-62.
[11]Xue N.Chinese word segmentation as character tagging[J].中文计算语言学期刊,2003,8(1):29-47.
[12]杨宪泽.21世纪高校特色教材,人工智能与机器翻译[M].成都:西南交通大学出版社,2006.
[13]Zhang H.The optimality of naive bayes.[C]//Seventeenth International Florida Artificial Intelligence Research Society Conference,Miami Beach,Florida,Usa.2004.
[14]Schwenker F.Hierarchical support vector machines for multi-class pattern recognition[C]//International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies,2000.Proceedings.IEEE,2000:561-565 vol.2.
[15]Osuna E,Freund R,Girosi F.Training support vector machines:an application to face detection[C]//Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,1997:130-136.
[16]忻栋,杨莹春,吴朝晖.基于SVM-HMM混合模型的说话人确认[J].计算机辅助设计与图形学学报,2002,14(11):1080-1082.
[17]Liu P,Qiu X,Huang X.Recurrent neural network for text classification with multi-task learning[C]//International Joint Conference on Artificial Intelligence.AAAI Press,2016:2873-2879.
[18]Kalchbrenner N,Grefenstette E,Blunsom P.A convolutional neural network for modelling sentences[EB].eprint ar X-iv:1404.2188v1,2014.
[19]Kim Y.Convolutional neural networks for sentence classification[EB].eprint ar Xiv:1408.5882,2014.
[20]Joulin A,Grave E,Bojanowski P,et al.Bag of tricks for efficient text classification[J].eprint ar Xiv:1607.01759,2016:427-431.
[21]Shamma D A,Shamma D A,Friedland G,et al.YF-CC100M:the new data in multimedia research[J].Communications of the Acm,2016,59(2):64-73.
[22]Luhn H P.The automatic creation of literature abstracts[M].IBM Journal of Research and Development,1958,2(2):159-165.