Affiliation:
1. Department of Informatics, Ionian University, 7 Tsirigoti Square, 49100 Corfu, Greece
Abstract
Simplicity in information found online is in demand from diverse user groups seeking better text comprehension and consumption of information in an easy and timely manner. Readability assessment, particularly at the sentence level, plays a vital role in aiding specific demographics, such as language learners. In this paper, we research model evaluation metrics, strategies for model creation, and the predictive capacity of features and feature sets in assessing readability based on sentence complexity. Our primary objective is to classify sentences as either simple or complex, shifting the focus from entire paragraphs or texts to individual sentences. We approach this challenge as both a classification and clustering task. Additionally, we emphasize our tests on shallow features that, despite their simplistic nature and ease of use, seem to yield decent results. Leveraging the TextStat Python library and the WEKA toolkit, we employ a wide variety of shallow features and classifiers. By comparing the outcomes across different models, algorithms, and feature sets, we aim to offer valuable insights into optimizing the setup. We draw our data from sentences sourced from Wikipedia’s corpus, a widely accessed online encyclopedia catering to a broad audience. We strive to take a deeper look at what leads to greater readability classification in datasets that appeal to audiences such as Wikipedia’s, assisting in the development of improved models and new features for future applications with low feature extraction/processing times.
Reference36 articles.
1. Zervopoulos, A., Alvanou, A.G., Bezas, K., Papamichail, A., Maragoudakis, M., and Kermanidis, K. (2020). Hong Kong Protests: Using Natural Language Processing for Fake News Detection on Twitter. IFIP Advances in Information and Communication Technology, Springer International Publishing.
2. Nikiforos, M.N., Deliveri, K., Kermanidis, K.L., and Pateli, A. (2022, January 3–4). Machine Learning on Wikipedia Text for the Automatic Identification of Vocational Domains of Significance for Displaced Communities. Proceedings of the 2022 17th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP), Corfu, Greece.
3. Mouratidis, D., and Kermanidis, K. (2019). Ensemble and Deep Learning for Language-Independent Automatic Selection of Parallel Data. Algorithms, 12.
4. Zhang, L., Liu, Z., and Ni, J. (2013, January 20–22). Feature-Based Assessment of Text Readability. Proceedings of the 2013 Seventh International Conference on Internet Computing for Engineering and Science, Shanghai, China.
5. Feng, L., Jansche, M., Huenerfauth, M., and Elhadad, N. (2010, January 23–27). A Comparison of Features for Automatic Readability Assessment. Proceedings of the 23rd International Conference on Computational Linguistics, COLING ’10, Beijing China.