Automated readability index.

By E. A. Smith, R. Senter. 1967

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xu Chen
2. Yongfeng Zhang
3. Cynthia C. S. Liem
4. Ivan Habernal
5. Iryna Gurevych
6. Markus Langer
7. Carrie J. Cai
8. Andrew M. Demetriou

Citations in the corpus

• Explainable Recommendation: A Survey and New Perspectives (2018, background): “, 1975), Automated Readability Index (Senter and Smith, 1967), and Smog Index (Mc Laughlin, 1969).”
• Human-Explainable Features for Job Candidate Screening Prediction (2017, methodology): “More specifically, we used 8 measures as features for the Readability representation: ARI [22], Flesch Reading Ease [12], FleschKincaid Grade Level [15], Gunning Fog Index [13], SMOG Index [16], Coleman Liau Index [8], LIX, and RIX [2].”
• Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media (2016, unknown): “For this measure, we compute the fraction of nouns, verbs2, and adverbs in posts and comments; automated readability index, a measure to gauge the understandability of text [62]; and linguistic accommodation, a process by which individuals in a conversation adjust their language style according to t”
• What makes a useful online review? Implication for travel product websites. (2015, background): “Another well-known readability test, namely the Automated Readability Index (ARI), focuses on the simple approach of calculating the amount of words and characters to evaluate the given text’s readability (Senter & Smith, 1967).”
• Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening (2018, methodology): “approximated by calculating several Readability indexes over the spoken transcripts: ARI (Smith and Senter 1967), Flesch Reading Ease (Flesch 1948), Flesch-Kincaid Grade Level (Kincaid et al.”
• Chain Reactions: The Impact of Order on Microtask Chains (2016, methodology): “Of the remaining 3115 sentences, we kept only those that were relatively complex by calculating the Automated Readability Index [42] and selecting 300 sentences at around the 75 percentile, with an Automated Readability Index of 18 – 23.”
• Argumentation Mining in User-Generated Web Discourse (2016, methodology): “The art of argumentation has been studied since the early work of Aristotle, dating back to the 4th century BCE (Aristotle and Kennedy [translator] 1991).”
• Identity Deception Detection (2017, unknown): “Automatic Readability Index (ARI) (Kincaid et al., 1975; Senter and Smith, 1967)”