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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 Synthetic, Yet Natural: Properties of WordNet Random Walk Corpora and the impact of rare words on embedding performance Klubicka, Filip; Mahalunkar, Abhijit; Maldonado, Alfredo... In: Conference papers (2019) BASE Show details 2 Phonetic acquisition in cortical dynamics, a computational approach Thiruvathukal, George K.; Wainselboim, Alejandro; Rizzi, Silvio... In: Computer Science: Faculty Publications and Other Works (2019) BASE Show details 3 Practical Natural Language Generation from Knowledge Graphs Harrison, Henry; Onnen, James; Fitchett, Lee... In: Embargoed Honors Theses, University of Nebraska-Lincoln (2019) BASE Show details 4 Efficient Inference, Search and Evaluation for Latent Variable Models of Text with Applications to Information Retrieval and Machine Translation Krstovski, Kriste In: Doctoral Dissertations (2016) BASE Show details 5 A Study on the Efficacy of Sentiment Analysis in Author Attribution Schneider, Michael J In: Electronic Theses and Dissertations (2015) Abstract: The field of authorship attribution seeks to characterize an author’s writing style well enough to determine whether he or she has written a text of interest. One subfield of authorship attribution, stylometry, seeks to find the necessary literary attributes to quantify an author’s writing style. The research presented here sought to determine the efficacy of sentiment analysis as a new stylometric feature, by comparing its performance in attributing authorship against the performance of traditional stylometric features. Experimentation, with a corpus of sci-fi texts, found sentiment analysis to have a much lower performance in assigning authorship than the traditional stylometric features. Keyword: Authorship Attribution; Computational Linguistics; Data Mining; NLP; Numerical Analysis and Scientific Computing; Sentiment Analysis; Stylometry URL: https://dc.etsu.edu/etd/2538 https://dc.etsu.edu/cgi/viewcontent.cgi?article=3915&context=etd BASE Hide details 6 CSC Senior Project: NLPStats Mease, Michael In: Computer Science and Software Engineering (2013) BASE Show details 7 Multilingual Vandalism Detection Using Language-Independent & Ex Post Facto Evidence West, Andrew G.; Lee, Insup In: Departmental Papers (CIS) (2011) BASE Show details 8 Software Internationalization: A Framework Validated Against Industry Requirements for Computer Science and Software Engineering Programs Vũ, John Huân In: Master's Theses (2010) BASE Show details

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