K
Kristopher Kyle
Researcher at University of Oregon
Publications - 54
Citations - 2245
Kristopher Kyle is an academic researcher from University of Oregon. The author has contributed to research in topics: Computer science & Vocabulary. The author has an hindex of 19, co-authored 46 publications receiving 1450 citations. Previous affiliations of Kristopher Kyle include University of Hawaii at Manoa & Georgia State University.
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Automatically Assessing Lexical Sophistication: Indices, Tools, Findings, and Application
TL;DR: The Tool for the Automatic Analysis of LExical Sophistication (TAALES), which calculates text scores for 135 classic and newly developed lexical indices related to word frequency, range, bigram and trigram frequency, academic language, and psycholinguistic word information, is introduced.
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The tool for the automatic analysis of text cohesion (TAACO): Automatic assessment of local, global, and text cohesion
TL;DR: P predictive validation of TAACO is provided and the notion that expert judgments of text coherence and quality are either negatively correlated or not predicted by local and overall text cohesion indices, but are positively predicted by global indices of cohesion is supported.
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Sentiment Analysis and Social Cognition Engine (SEANCE): An automatic tool for sentiment, social cognition, and social-order analysis
TL;DR: This study introduces the Sentiment Analysis and Cognition Engine (SEANCE), a freely available text analysis tool that is easy to use, works on most operating systems, is housed on a user’s hard drive, allows for batch processing of text files, and includes negation and part-of-speech (POS) features.
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The tool for the automatic analysis of lexical sophistication (TAALES): version 2.0
TL;DR: Newly added TAALes 2.0 indices, including those related to n-gram association strength, word neighborhood, and word recognition norms, featured heavily in these predictor models, suggesting that TAALES 2.1 represents a substantial upgrade.
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Measuring Syntactic Complexity in L2 Writing Using Fine-Grained Clausal and Phrasal Indices.
TL;DR: Fine‐grained indices of phrasal complexity were better predictors of writing quality than either traditional or fine-grained clausal indices, though a single fine‐ grained index of clausal complexity contributed to the combined model, providing some support for Biber et al.