Thesis (D.A.)--Illinois State University, 1995. / Title from title page screen, viewed May 4, 2006. Dissertation Committee: Douglas Hesse (chair), Janice Neuleib, Maurice Scharton. Includes bibliographical references (leaves 178-182) and abstract. Also available in print.

The question of the appropriateness and effectiveness of students' personal writing is a longstanding one in the academy. In composition studies, the ideological fight over personal and academic writing is most often represented by the oft-studied but rarely changed Bartholomae/Elbow debate. In literary studies, reader-response critics in particular have wrestled with the problems and possibilities of subjective interpretation. Yet despite scholastic interest in issues of personal writing, discussions have remained primarily theoretical and have relied mainly on anecdotal evidence. While small-scale case studies valuably illuminate the processes of an individual student or two, the conversation would be profoundly bolstered by empirical data. How common are personal responses, really? Further, while many believe that any presence of first-person pronouns signals personal, subjective writing, anecdotal cases suggest that there are several categories of personal writing, and that these different types of expressivism produce a range of rhetorical effects. The current study attempts to name and refine these categories--using the distinctions of General claim, Writer-based prose, Personal experience, and Personal claim—to begin to fill in this empirical gap. Is it a mistake to lump all use of personal reference into the category of "personal writing"? Would helping students distinguish between these varying types of personal references inform their stylistic and rhetorical choices? By reviewing a sample of 30 short papers written by college students in a general requirement literature survey course, I will examine how frequently--and in what ways--students reference themselves when responding in writing to a work of literature. / text

Spelling normalization is the task to normalize non-standard words into standard words in texts, resulting in a decrease in out-of-vocabulary (OOV) words in texts for natural language processing (NLP) tasks such as information retrieval, machine translation, and opinion mining, improving the performance of various NLP applications on normalized texts. In this thesis, we explore different methods for spelling normalization of English student writings including traditional Levenshtein edit distance comparison, phonetic similarity comparison, character-based Statistical Machine Translation (SMT) and character-based Neural Machine Translation (NMT) methods. An important improvement of our implementation is that we develop an approach combining Levenshtein edit distance and phonetic similarity methods with added components of frequency count and compound splitting and it is evaluated as a best approach with 0.329% accuracy improvement and 63.63% error reduction on the original unnormalized test set.