本文選題：智能作文評閱 + Adaboost/CT　； 參考：《濟(jì)南大學(xué)》2017年碩士論文

【摘要】：英語教學(xué)堪稱國內(nèi)教學(xué)之重之難。其重要性在高考、考研、考博、出國留學(xué)等人生重要考試中的地位已無須多言;其困難性在于環(huán)境與師資的嚴(yán)重不足。以英語作文為例,學(xué)生要想在英語作文上有較大的提高,需要得到教師對學(xué)生作文進(jìn)行有效的批改。這種教學(xué)上的內(nèi)在需求,不僅僅要求英語教師花費(fèi)大量的時間去批改作文,還要求教師有較高的英語寫作水平。目前國內(nèi)的英語教師基本上為本土教師,母語并非英語,無論是水平上,還是精力上都不能勝任這項(xiàng)工作。在實(shí)際的英語寫作教學(xué)過程中,常見的情形是:學(xué)生人數(shù)多,教師人數(shù)少,閱卷效率低,“從考試到閱卷到反饋”的周期太長,學(xué)生寫作練習(xí)機(jī)會太少,反饋滯后致使不足之處得不到及時糾正提高等普遍存在的現(xiàn)象,不一而足。除此之外,在閱卷的過程中,閱卷教師給出的得分很容易受到主觀情緒等不利因素的影響,甚至以偏概全,給出比較極端的判卷結(jié)果。隨著計(jì)算智能的長足發(fā)展,近年來“機(jī)器閱卷”已走入大眾視野。它克服了人工閱卷效率低的不足,彌補(bǔ)了摻雜教師情感的缺陷,保證了閱卷的準(zhǔn)確高效以及評價的客觀性和一致性,將教師從繁重的勞動力中解脫出來,以便于做更有意義的工作。國外對于寫作智能評閱的研究比較早,并且已有比較成熟的系統(tǒng),實(shí)際的應(yīng)用中表現(xiàn)出了較好的可靠性,但這些系統(tǒng)的設(shè)計(jì)都是針對以英語作為母語寫作來進(jìn)行評閱的,對于國內(nèi)的英語考試,考慮到考生和閱卷老師母語都不是英語,且針對英語寫作能力的要求也不同于英語作為母語的要求,若套用這些系統(tǒng)進(jìn)行評閱,必然存在于人工評閱的“不兼容”,因此針對國內(nèi)學(xué)生及其文化的訴求,作文智能評閱算法在指標(biāo)選取與反饋中作了改進(jìn)。本研究以英語作文智能評分算法為探索背景,收集真實(shí)考試的樣本試卷,研究可能影響作文結(jié)果的閱卷指標(biāo),歸納對作文分?jǐn)?shù)影響顯著的算法指標(biāo),提取出考試的一般規(guī)律,在此基礎(chǔ)上建立一個分層的閱卷模型。通過綜合考慮英語四級作文的評分標(biāo)準(zhǔn),以及借鑒前人總結(jié)的一些指標(biāo),構(gòu)建了一套指標(biāo)體系,考慮到構(gòu)成作文的基本要素是單詞、句子和篇章結(jié)構(gòu)三大方面的指標(biāo),通過各個指標(biāo)跟作文分?jǐn)?shù)進(jìn)行主成分分析,挖掘出影響作用顯著的指標(biāo),并對這些指標(biāo)進(jìn)行分析,根據(jù)指標(biāo)與作文分?jǐn)?shù)的線性擬合,得到一個最佳的值,構(gòu)建一個分層的評閱模型。然而作文分?jǐn)?shù)不是最終的目的,本研究提出的智能評閱系統(tǒng)不僅僅包括評閱分?jǐn)?shù),還包括分析信息的自然語言反饋以及個人學(xué)習(xí)建議,從而達(dá)到以評促學(xué)的目的。本系統(tǒng)通過三種工具計(jì)算潛在影響作用的指標(biāo),對來自不同專業(yè)和班級的三種話題312篇作文進(jìn)行了測試。實(shí)驗(yàn)表明,智能作文評閱與人工評閱相對比的精確準(zhǔn)確性為79.66%,鄰接準(zhǔn)確率為94%,最大誤差率均小于20%,智能評分系統(tǒng)不存在奇異值性誤差。結(jié)果表明改進(jìn)后的Adaboost/CT算法能夠很好地應(yīng)用于智能作文評分。
[Abstract]:The importance of English teaching is very difficult in domestic teaching. The importance of English teaching is not necessary in the important examinations of life, such as college entrance examination, entrance examination, examination and study abroad. The difficulty lies in the serious shortage of environment and teachers. The internal demand of this kind of teaching is not only required by English teachers to spend a lot of time to correct their compositions, but also to require teachers to have a higher level of English writing. At present, English teachers in China are basically native teachers, their mother tongue is not English, both at the level and in their energy. In the course of English writing teaching, the common situation is: the number of students, the number of teachers, the low reading efficiency, the long period from examination to reading to the feedback, the students' writing practice is too little, and the feedback lag causes the deficiency to be corrected and raised in time. In the process, the scores given by the teachers are easily affected by the adverse factors such as subjective emotion, even to the extreme. With the rapid development of the computational intelligence, the "machine reading" has entered the public field of vision in recent years. It overcomes the shortage of manual reading efficiency and makes up for the emotion of adulterant teachers. Defects, which guarantee the accuracy and efficiency of the marking and the objectivity and consistency of the evaluation, release the teachers from the heavy labor force in order to make it easier to do more meaningful work. The design of the system is aimed at reading English as a native language. For the English test in China, the native language of the examinee and the reading teacher is not English, and the requirements for the English writing ability are different from the English language as a mother tongue. Therefore, in view of the demands of domestic students and their culture, the composition intelligent evaluation algorithm has been improved in the selection and feedback of the index. This study takes the English composition intelligent scoring algorithm as the exploration background, collects the sample test papers of the true examination, studies the reading index which may affect the composition results, and sums up the algorithms that have a significant influence on the composition scores. On the basis of the general rules of the examination, a hierarchical reading paper model is set up, and a set of index system is built through the comprehensive consideration of the scoring standard of four grades of English composition and some indexes summed up by the predecessors. The basic elements of the composition are the indicators of the three aspects of words, sentences and text structures. Each index and composition score are analyzed by principal component analysis, and the index is excavated, and the indexes are analyzed. According to the linear fitting of the index and composition score, an optimal value is obtained and a hierarchical review model is constructed. However, the composition score is not the ultimate goal, the intelligent review system proposed in this study It includes not only the evaluation score, but also the natural language feedback and personal study advice of the analysis information, so as to achieve the goal of learning and promoting learning. The system uses three tools to calculate the indicators of potential impact, and tests 312 compositions from three topics from different majors and classes. The accuracy of the artificial review is 79.66%, the adjacency accuracy is 94%, the maximum error rate is less than 20%, and the intelligent scoring system does not have the singular value error. The results show that the improved Adaboost/CT algorithm can be well applied to the intelligent composition score.
【學(xué)位授予單位】：濟(jì)南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：H319;TP391.1

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本文編號：1933676