- 期刊
- OpenAccess
Assessing Text Readability Using Hierarchical Lexical Relations Retrieved from WordNet
並列摘要
Although some traditional readability formulas have shown high predictive validity in the r=0.8 range and above (Chall & Dale, 1995), they are generally not based on genuine linguistic processing factors, but on statistical correlations (Crossley et al., 2008). Improvement of readability assessment should focus on finding variables that truly represent the comprehensibility of text as well as the indices that accurately measure the correlations. In this study, we explore the hierarchical relations between lexical items based on the conceptual categories advanced from Prototype Theory (Rosch et al., 1976). According to this theory and its development, basic level words like guitar represent the objects humans interact with most readily. They are acquired by children earlier than their superordinate words like stringed instrument and their subordinate words like acoustic guitar. Accordingly, the readability of a text is presumably associated with the ratio of basic level words it contains. WordNet (Fellbaum, 1998), a network of meaningfully related words, provides the best online open source database for studying such lexical relations. Our study shows that a basic level noun can be identified by its ratio of forming compounds (e.g. chair→armchair) and the length difference in relation to its hyponyms. We compared graded readings for American children and high school English readings for Taiwanese students by several readability formulas and in terms of basic level noun ratios (i.e. the number of basic level noun types divided by the number of noun types in a text). It is suggested that basic level noun ratios provide a robust and meaningful index of lexical complexity, which is directly associated with text readability.
參考文獻
被引用紀錄
延伸閱讀
- 王慧茹（2018）。A Case study of Expressible Literacy in ＂Heterogeneous Text＂。全人教育學報，(16)，27-49。https://doi.org/10.6272/JHE.201805_(16).0002
- Zhai, Z. (2021). Research on Text Sentiment Analysis based on Attention Mechanism. International Core Journal of Engineering, 7(8), 211-216. https://doi.org/10.6919/ICJE.202108_7(8).0030
- Hong, J. F., Sung, Y. T., Tseng, H. C., Chang, K. E., & Chen, J. L. (2016). A multilevel analysis of the linguistic features affecting Chinese text readability. 臺灣華語教學研究, (13), 95-126. https://doi.org/10.29748/TJCSL.201612_(13).0005
- Sanglerdsinlapachai, N., Plangprasopchok, A., & Nantajeewarawat, E. (2017). Exploring Hierarchical Linguistic Structure for Aspect-Based Sentiment Analysis. 網際網路技術學刊, 18(4), 945-952. https://doi.org/10.6138/JIT.2017.18.4.20170429f
- 許峰益（2004）。A Study of Text Classification Using Co-occurrence Term Analyzing in A Topic Corpus〔碩士論文，元智大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0009-0112200611305141