Computer scientists, linguists, stylometricians, and cognitive scientists have successfully divided corpora into modes, domains, genres, registers, and authors. The limitations for these successes, however, often result from insufficient indices with which their corpora are analyzed. In this paper, we use Coh-Metrix, a computational tool that analyzes text on over 200 indices of cohesion and difficulty. We demonstrate how, with the benefit of statistical analysis, texts can be analyzed for subtle, yet meaningful differences. In this paper, we report evidence that authors within the same register can be computationally distinguished despite evidence that stylistic markers can also shift significantly over time
Natural language processing tools, such as Coh-Metrix and LIWC, have been tremendously successful in...
It is of great significance to discriminate good writing from poor writing and Coh-Metrix, a computa...
AbstractThis paper presents some experiments in the task of authorship attribution. We achieve this ...
Computer scientists, linguists, stylometricians, and cognitive scientists have successfully divided ...
Advances in computational linguistics and discourse processing have made it possible to automate man...
Recent research in text processing has emphasized the importance of the cohesion of a text in compre...
Text classification remains one of the major fields of research in natural language processing. This...
This software paper describes ‘Stylometry with R’ (stylo), a flexible R package for the high-level a...
Writers are often viewed as having an inherent style which can serve as a literary fingerprint. By q...
Statistical methods have been widely employed in many practical natural language processing applicat...
grantor: University of TorontoOne of the most difficult tasks facing anyone who must compi...
Along with its methodological development, authorship analysis has expanded in scope to new applicat...
Each individual has a distinguished writing style. But natural language generation systems pro- duce...
Abstract: In this project, we developed an Artificial Intelligence (AI) that takes a document and c...
The statistical analysis of the heterogeneity of the style of a text often leads to the analysis of ...
Natural language processing tools, such as Coh-Metrix and LIWC, have been tremendously successful in...
It is of great significance to discriminate good writing from poor writing and Coh-Metrix, a computa...
AbstractThis paper presents some experiments in the task of authorship attribution. We achieve this ...
Computer scientists, linguists, stylometricians, and cognitive scientists have successfully divided ...
Advances in computational linguistics and discourse processing have made it possible to automate man...
Recent research in text processing has emphasized the importance of the cohesion of a text in compre...
Text classification remains one of the major fields of research in natural language processing. This...
This software paper describes ‘Stylometry with R’ (stylo), a flexible R package for the high-level a...
Writers are often viewed as having an inherent style which can serve as a literary fingerprint. By q...
Statistical methods have been widely employed in many practical natural language processing applicat...
grantor: University of TorontoOne of the most difficult tasks facing anyone who must compi...
Along with its methodological development, authorship analysis has expanded in scope to new applicat...
Each individual has a distinguished writing style. But natural language generation systems pro- duce...
Abstract: In this project, we developed an Artificial Intelligence (AI) that takes a document and c...
The statistical analysis of the heterogeneity of the style of a text often leads to the analysis of ...
Natural language processing tools, such as Coh-Metrix and LIWC, have been tremendously successful in...
It is of great significance to discriminate good writing from poor writing and Coh-Metrix, a computa...
AbstractThis paper presents some experiments in the task of authorship attribution. We achieve this ...