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  • 學位論文

可讀性分析在特徵選擇上作探討與研究

On the Study of Feature Selection for Readability Analysis

指導教授 : 郭經華

摘要


隨者網路的發達,我們可以在各大英文教學網站取得學習的閱讀資料,這些閱讀資料的難易度都是由該網站的負責人所定義的,對於其他沒有定義的資料就不知它的閱讀難度,所以我們要找出一個可以區分文章難易程度的方法,讓學習者找出適合自己的閱讀教材。 本論文為英語為第二外語的使用者找出一些可以分析英語文章難易度的特徵,如句子長度、單字、文法等因素,並把這些特徵結合用以分析兩篇閱讀文章彼此之間的難易度差距,方便讓學習者找出適合自己程度的閱讀資料,以達到學習的效果。本研究和以往相關文獻的差異在於多了文法分析,以往的研究大部分都在於用單字來分辨難度,在此我們利用POS Tagger來分析每句句子的詞性,接著再對句子作切割以找出可以辨別文章可讀性難易度差異的文法因素,最後在和上述的特徵作合併以計算兩篇文章的難度差距。

並列摘要


As the capability of the internet is growing, we can get reading resources for learning English from popular websites. The readability of these reading resources was confirmed by the person in charge of these websites, but for other resources not confirmed is unknown. Thus we need a method to obtain the readability of these remaining resources to make learners to find their own reading materials. Main motivation of this thesis presents features of analyzing difficulty by length of sentences, vocabulary, and grammar for ESL learner. Interpret texts with these features leads to an appropriate differentiation for learner in finding the proper text and generate to the efficiency result. The difference between our research and related works is that our research applies grammar analysis; those works mostly recognize readability with single terms. First we tag the POS of each word with POS tagger, and we cut these sentences to find out the factors which support us in recognizing the readability in grammar, and then combine the features above to calculate the difference of difficulty.

參考文獻


[6]Kevyn Collins-Thompson is a Researcher in the Context, Learning, and User Experience for Search group at Microsoft Research (Redmond).
[1]Chall, J.S. 1958. Readability: An appraisal of research and application. Bureau of Educational Research Monographs, No. 34. Columbus, OH: Ohio State Univ. Press.
[2]Klare, G. R. 1963. The Measurement of Readability. Ames,IA. Iowa State University Press.
26(6):490–496
[5]M Coleman and T. Liau. 1975. A computer readability formula designed for machine scoring. Journal of Applied Psychology, 60:283–284.

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