Stylistics

Combining Corpus-Based Features for Selecting Best Natural Language Sentences

Authorship: 
Foaad Khosmood and Robert Levinson
Publication: 
International Conference on Machine Learning and Applications
Conference
Location: 
Honolulu, Hawaii
December, 2011
Automated paraphrasing of natural language text has many interesting applications from aiding in better translations to generating better and more appropriate style language. In this paper, we are concerned with the problem of picking the best English sentence out of a set of machine generated paraphrase sentences, each designed to express the same content as a human generated original. We present a system of scoring sentences based on examples in large corpora.

Taxonomy and Evaluation of Markers for Computational Stylistics

Authorship: 
Foaad Khosmood and Robert Levinson
Publication: 
International Conference on Artificial Intelligence (ICAI)
Conference
Location: 
Las Vegas, Nevada
July, 2011
Currently, stylistic analysis of natural language texts is achieved through a wide variety of techniques containing many different algorithms, feature sets and collection methods. Most machine-learning methods rely on feature extraction to model the text and perform classification. But what are the best features for making style based distinctions? While many researchers have developed particular collections of style features – called style markers – no definitive list exists.

Toward Automated Stylistic Transformation of Natural Language Text

Authorship: 
Foaad Khosmood and Robert Levinson
Publication: 
Digital Humanities
Conference
Location: 
University of Maryland College Park
June, 2009
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